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Small Unmanned Surface Vessels—A Review and Critical Analysis of Relations to Safety and Safety Assurance of Larger Autonomous Ships

Victor Bolbot
Andrei Sandru
Ture Saarniniemi
Otto Puolakka
Pentti Kujala
1,4 and
Osiris A. Valdez Banda
Department of Mechanical Engineering (Marine Technology), Aalto University, 02150 Espoo, Finland
Kotka Maritime Research Centre, 48100 Kotka, Finland
Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland
Estonian Maritime Academy, Tallinn University of Technology, 11712 Tallinn, Estonia
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2023, 11(12), 2387;
Submission received: 31 October 2023 / Revised: 9 December 2023 / Accepted: 14 December 2023 / Published: 18 December 2023


Autonomous ships represent an emerging paradigm within the maritime sector, poised to bring multiple advantages. Although numerous prototypes have been developed, the deployment of large autonomous ships has predominantly remained confined to domestic waters or specialized military applications. The extensive adoption of autonomous ships is hampered by several challenges, primarily centered around safety. However, the direct assessment of autonomous technologies on large-scale vessels can be very costly. Small-scale autonomy testing may provide a cheaper option. This study reviews the current small autonomous ship models used by maritime researchers and industry practitioners. It aims to evaluate how these autonomous models currently augment and can augment safety assurances on larger autonomous ships. The review identifies relevant very small Unmanned Surface Vessels (USVs), the main research groups behind them and their applications. Then, the current use of USVs for safety and safety assurance is analyzed. Finally, the paper suggests innovative strategies and research directions for using USVs for the safety assurance of larger autonomous ships.

1. Introduction

Autonomous ships are on the horizon [1], with numerous prototypes emerging in the maritime industry, showcasing a range of autonomous and remote-control capabilities [2,3,4,5,6,7,8]. These vessels are poised to enhance safety by reducing crew exposure to hazards [9] and reducing the likelihood of human errors in certain accident scenarios [10]. Additionally, they contribute to environmental sustainability by optimizing cargo and space allocation [11] and leveraging digital technologies necessary for energy efficiency, while also promoting gender equality [12]. The deployment of autonomous ships in the Arctic region holds the promise of improving operational efficiency and safety [13,14].
Nonetheless, autonomous ships adoption is slow, which is primarily attributed to a multitude of challenges, including regulatory hurdles, safety concerns, security issues, and cybersecurity threats [7,8,15]. Among these challenges, ensuring the reliability of collision avoidance systems stands out [16,17,18,19] since this is a key enabling system that must be rendered safe for the successful implementation of autonomous ships. Testing collision avoidance and the associated situational awareness systems presents its own set of difficulties, given that full-scale systems are inherently costly and the testing process can prove to be intractable [16,20,21]. Moreover, sea and shop trials typically occur during the later stages of design, which can significantly increase the expenses associated with detecting and rectifying errors, as well [22].
In this regard, simulation-based approaches offer a promising way to expedite the verification process and the development of safety processes [20,21,23]. However, it is important to acknowledge that simulation-based methods have their limitations, as they rely on approximations of real-world environments and natural phenomena [24].
Alternatively, ship models can be leveraged to advance the development and testing of such technologies. The use of ship models for ship design, hydrodynamic analysis and ice resistance calculations has taken place since the late 19th century, with pioneering works by W. Froude, Kashteljan and others [25,26]. At present, the Energy Efficiency Design Index [27], hull performance calculations [28], ice-breaking [29,30] and ice resistance [31] calculations heavily rely on towing tank tests of small, geometrically identical models. Hence, it is worth investigating whether autonomous ship models can be employed to support safety cases for their full-scale counterparts.
Small Unmanned Surface Vessels (USVs) have found utility in a variety of applications, including in the development and validation of prototype control algorithms under typical operational conditions across various ship design phases [32,33,34,35,36]. They have also been employed in ice-covered environments [37], aiding in the enhancement of positioning algorithms [38,39], identifying ship hull parameters [40,41], testing ship collision avoidance scenarios [42], tracking fish [43], and facilitating operations near the shoreline [44,45]. Furthermore, ship models have been integrated into the assessment of the performance of multiple vessels operating in tandem, under both normal and abnormal conditions, to substantiate safety claims at full scale [46].
Previous reviews on USV applications have been documented in [47,48,49,50,51,52], with some references to safety and security applications. It is essential to note that these studies can be largely considered outdated, as they were published over a decade ago. A more recent review on USVs’ state of the art systems, guidance, navigation and control techniques can be found in [53]. Yet, the safety and cybersecurity implications and applications were omitted from this study. Similarly a recently published review identified the key technologies in USVs, omitting to a large extent the safety and cybersecurity considerations [54]. In [55], 60 USVs of various size were identified to support the development of classifications of autonomy degree. The applications of USVs for disaster relief were reviewed in [56]. A recent comprehensive exploration of potential applications for USVs was presented in [57]. This review, conducted through a systematic and bibliometric literature analysis, incorporated a broad spectrum of applications, spanning from military to civilian domains. It also offered valuable recommendations for expanding the utilization of USVs, thereby contributing to their broader adoption and impact. Yet, it did not include specifics about their applications, architecture, equipment used or more detailed discussions on how they can contribute to safety.
So, notably, while small USVs have demonstrated their versatility in these domains, there remains a research gap concerning a detailed review of their architecture, use and potential contribution to safety and cybersecurity assurance. This paper endeavors to address this existing gap by comprehensively examining what small USVs are available, who are the researchers working on the small USVs, how the small USVs are currently utilized, especially in connection to safety, and how small USVs could theoretically contribute to maritime safety and cybersecurity assurance.
The primary scope of this investigation is directed towards civil applications, with a deliberate exclusion of military applications, considering the high sensitivity surrounding the topic. It is acknowledged that interested parties can readily access relevant information elsewhere [47,48,49,50,53,57,58,59,60,61]. Furthermore, the analysis within this study is specifically oriented towards very small USVs with an approximate displacement range of up to 100 kg, considering the logarithmic scale for USV classification in [49]. These very small USVs are analyzed in terms of their particulars, authors and authors’ cooperation networks, utility and prospective contributions to safety and whenever applications can be found, and also in relation to cybersecurity. It is worth noting that such USVs can be more easily operated by one or a maximum of four individuals, have substantially lower costs, and can concurrently serve educational and small-scale research objectives. In this way, the research findings can be of greater value to researchers who have limited budget and support. In the context of this paper, the term “USVs” encompasses both remotely controlled small vessels and those equipped with advanced autonomous capabilities [48]. Additionally, as most publications fail to disclose the cost associated with these USVs, cost information is not included in this review, but a lack of this information does not constitute a criterion for exclusion. Only the references that lacked substantial information, i.e., those related to the main particulars, were excluded. Also, publications from various sources were embraced, spanning from 2000 to 2023, not concentrating on Web of Science- or Scopus-related publications to incorporate to a greater extent perspectives from industry and researchers from poorer countries. It seems that this consideration serves the aim of the publication (finding the small USVs’ current utilization) much better. Those studies which used simulations and not real USVs for algorithms verification and validation were excluded as well, as they do not demonstrate a practical exploitation of the very small USVs.
The contribution of the article stems from answering three research questions (RQs): (1) What very small nonmilitary USVs can be identified from the literature, what are their main particulars and characteristics and who are the leading research groups working on them? (2) How have very small USVs been used? and in particular (3) How have they been used in the context of safety and cybersecurity assurance? Additionally, research directions for how to more effectively use the small USVs in the context of the safety and safety assurance of larger autonomous ships are proposed.

2. Methodological Approach

The methodological approach employed includes several sequential steps. First, it centers on identifying small USVs and their particulars and the relevant research groups (step 1), and subsequently delves into comprehending their current utilization based on pertinent data obtained from identified sources (step 2). Following this, the analysis investigates how the current fleet of small USVs is used for safety assurance and safety-related purposes (step 3). Finally, the study incorporates pre-existing safety and cybersecurity issues associated with autonomous shipping from the known review studies to enhance the identification of directions for potential future research concerning ship model applications in the context of safety and cybersecurity assurance. A visual representation of these methodological steps is provided in Figure 1.
The process of identifying small USVs relevant to the present study started with an analysis of the references provided in the prior review studies [47,48,49,50,53,54,55,57]. Subsequently, this list was expanded through targeted keyword searches on Google Scholar using keywords such as “small USVs in the maritime”, taking as input the first 50 responses from Google Scholar. Responses from OpenAI 3.5 regarding known USVs contributed to this list (“Can you refer to small USVs examples?”) [62]. To maintain alignment with the PRISMA methodology [63], references lacking adequate information or falling outside the predetermined scope of the present analysis were systematically eliminated, specifically focusing on small USVs for civil applications with displacement ranging approximately up to 100 kg. Also, references in languages other than English were excluded.
Bibliometric analysis tools such as VOS viewer 1.6.18 [64] were used to identify the leading authors in the area based on the full-counting method, which determines the strength of the link among the authors based on the number of joint publications. Metrics calculated using MS Excel were used to identify the most popular hull forms, hardware and software used in very small USVs, as well as countries associated with USV designers. For the countries’ metrics, in the case of multiple countries and authors, the first affiliation of the first author was used. As is demonstrated in the subsequent sections, such knowledge of authors and the USVs characteristics is important when discussing the use of small USVs for safety and cybersecurity assurance.
Following this, a second search was conducted, targeting leading authors of the USV-related publications from step 1, such as professors and permanent staff, by checking their profile publications on Google scholar and their research publications citing the original study, presenting the USVs already included in step 1. This secondary search aimed to deepen the understanding of the current utilization of the identified USVs. Also, this search contributed to the identification of additional relevant USVs, which is why a feedback loop from step 2 to step 1 in Figure 1 is provided. The generated database was then exploited to determine the current use of small USVs with greater rigor. To support the analysis, the terms map of the VOSviewer was exploited [64]. Furthermore, the references were systematically analyzed to identify their applications, as well as applications in ice-covered areas, and relevant metrics estimated using MS Excel.
During the third step using the identified USVs database, the ways that small USVs’ use is currently linked to safety and cybersecurity were investigated in greater depth. To that end, the previously identified authors networks and terms-map-based analysis were employed. Keywords related to safety were searched along the identified USV-related publications’ titles and abstracts, and relevant metrics were estimated using MS Excel. Based on the identified results, conclusions on the current link between safety and small USVs were derived.
It is worth noting that comprehensive reviews of safety and cybersecurity challenges pertaining to autonomous ships and underpinning the critical analysis step have been thoroughly covered in several earlier studies, as exemplified by references [1,7,8,15,65]. To maintain brevity and prevent redundancy with existing publications, we refrained from presenting this background information in the article itself. Instead, this foundational knowledge was directly incorporated into this analytical process. Interested readers can direct themselves to those articles.
Building upon this initial groundwork, including the identified USVs, their characteristics and use, authors’ networks and ideas regarding how small USVs can contribute to ensuring safety and bolstering cybersecurity in the realm of autonomous shipping were explored, which is one of the critical contributions of this research article.

3. Results

3.1. RQ1: Related Very Small USVs and Leading Research Groups

In the preceding decades, numerous USVs have been developed. USVs with dimensions significantly exceeding those set in the scope (significantly more than 100 kg displacement), as in references [5,40,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101], were excluded from this analysis (38+, and also many of the USVs referenced in [47,48,49,50,53,54,55,57]). Furthermore, this review did not include the USVs lacking sufficient specific information, such as those referenced in [44,102,103,104,105,106,107,108,109,110,111] and many other references (11+ in total). The security applications of USVs, as in [112,113,114,115,116] (5+), among many others uncited in this work, were also disregarded. A few references were inaccessible [117,118,119] (three in total), and thus not incorporated. The selected very small USVs and their primary characteristics and uses are detailed in the table in Appendix A (84 in total, with the last update day 30 September 2023), whilst the main highlights about the USVs and the leading researchers are provided in this section. The modest rate of inclusion can be ascribed to the relatively constrained emphasis placed by the researchers on the development of very small USVs (up to 100 kg) and the subsequent dissemination of associated research findings.
It can be argued that the database used, despite its limited scope, is of equal quality if not superior to that provided in other review studies. The analysis in [57] (involving 245 research studies) included review studies, military applications, studies with insufficient material and simulation studies, even datasets, which were excluded from the present analysis. Several of these studies (245) referred to the same vessel multiple times, as well, so in this way there were redundant applications in the present review (not counted multiple times but still included for other bibliometric analyses). Furthermore, the developed database also contained references from other reviews, which were not included in [57]. Moreover, this review encompassed 26 small USVs (Figure 2) and related research published starting from 2021, which was not included in [57]. So, this number of investigated small USVs (84) surpasses the quantity of small USVs examined in [57] and is also larger than in [55], despite focusing exclusively on very small USVs. This is attributable to the current review’s more contemporary nature, its reliance on previous reviews and the implementation of some quality checks.
A substantial number of small USVs has been developed and made publicly accessible through publications originating primarily from the United States, China, South Korea, Portugal, Italy and The Netherlands (Figure 3). These countries alone contributed to 50 out of 84 identified small USVs (60%), albeit with USVs from a total of 28 countries being included in Appendix A. Among the research institutions, it is noteworthy that TuDelft from The Netherlands, KAIST from South Korea, and the University of Porto in Portugal have designed the highest single number of identified very small USVs.
Almost half (41/85) of the USVs were catamarans or trimarans (Figure 4). Podded azimuth propulsors were reported to be in use several times, while others indicated the use of propellers and rudders. It is important to note that a subset of these USVs did not provide any information on propulsors, so this information type suffers from uncertainty. None of the very small USVs were engine powered, and they either had a battery pack or relied on solar panels and wave energy.
In terms of navigational systems, a vast majority of these models heavily relied on the Global Navigation Satellite System (GNSS) (in 83% of the cases) for the positioning and speed measurement. Additionally, many models employed an Inertial Measurement Unit (IMU) (44%), although gyros and compasses were also commonly employed (22%). Furthermore, for the positioning and speed estimation and also object detection, laser scanners (i.e., LiDARs) (17%) and various types of camera (32%) were commonly cited components in these applications, although some references to the use of AIS (Automatic Identification System) (2%) were also made.
Based on the available but limited data, it is evident that among the software libraries and middleware, the Robot Operating System (ROS) variants emerge as the most popular choice (20% of all USVs), especially in the latest USVs. This middleware is followed by LabVIEW (7%) and by ArduPilot (4%). Within the ROS framework, C++ serves as the predominant programming language (7%), with a smaller number of USVs specifying the use of Python (5%) and JAVA (2%). The use of various Raspberry Pi hardware solutions was reported in 13% of the cases. However, it can be assumed that the use of ROS, Labview, ArduPilot, C++, Python and Raspberry Pi was more frequent, since not all the USVs included the complete hardware and software information.
The bibliometric analysis of the authors’ network implemented using VOSviewer and the full counting method (which emphasizes the strength of links based on the number of joint publications) for the 183 references from Appendix A is provided in Figure 5. The letters’ size and the radius of the bubbles are proportional to the number of documents published by the various authors, whilst the colors correlate with the year of publication.
The largest group in the network is the one connecting Brazil, with leading author Dr. Mathaus Ferreira da Silva, and Portugal, with leading authors Prof Nuno Cruz and Prof José Carlos Alves, both from the University of Porto. Other notable groups of researchers with active publishing on small USVs include groups from Italy, with Dr. Angelo Odetti and Mr. Gabriele Bruzzone among the leading authors, the group with prominent author Prof Jorge Cabrera Gamez from IUSIANI in Spain, the group with Dr. Donghoon Kim and Prof Myung Hyun from KAIST in South Korea, the group from Norway with leading author Prof Fossen from NTNU, Mr. Antonio Vasilijevic from Croatia (University of Zagreb) and the group from The Netherlands with leading author Prof. Rudy Negenborn from TuDelft.
These identified groups correlate well with the leading countries of Figure 3, albeit there are some notable differences. For instance, the research on small USVs in the USA seems to be scattered among different organizations with no large interconnected group of researchers or persistent publications in the area. This finding is similar to the one presented in a bibliometric analysis on maritime cybersecurity [15]. Similarly in China there seems to be several different groups of researchers working on small USVs mostly independently from each other and located in Wuhan University of Technology, Northwestern Polytechnical University and Dalian Maritime University. Similar observations can be made about the South Korea and Italy, where multiple disconnected (based on publications) research groups are present.
As it can be also observed, the research groups were largely detached from each other, but this was also anticipated from other reviews [15,120]. This is also in line with Figure 3, where it was found that authors stem from 28 different countries, so it is expected that the co-authorship network will be largely distributed in small “islands”.

3.2. RQ2: Very Small USV Identified Use

A term map analysis was conducted using VOSviewer, based on the information available in the titles and abstracts of the 183 papers provided in Appendix A. In developing this map, a binary counting method was employed. In binary counting method, the occurrences emphasize the number of documents that a term appears. So, the larger the occurrences number in the documents, the larger the radius of the circle associated with the term. Lines are used to connect the terms that frequently appear together. The different colors are used to characterize the cluster of terms that frequently appear together.
Only terms that occurred at least three times were included. General terms such as “methodology”, “water”, “technology” were intentionally excluded from the map, as they do not contribute to the analysis purpose. Additionally, the term “USV” and relevant terms were excluded from the analysis, as the focus was on identifying other relevant terms, given that the frequent appearance of “USV” and its synonyms was expected. In this fashion more than 200 frequently appearing general terms were eliminated or merged during the preparation for terms analysis.
Finally, during the terms analysis, only the 95% most relevant terms were used, with relevance score calculated by VOSviewer. The terms were clustered in various groups using the default VOSviewer settings.
Out of 4115 terms present in the titles and the abstracts, only 148 met the set criteria and the results are presented in Figure 6. As it can be observed, the terms such as ”algorithm”, “path”, “controller”, “collision avoidance”, “sensors” are the most frequently occurring terms in the database. Notably, sensors employed in USVs are also observant on the map or in the 148 selected keywords (“camera”, “LiDAR”, “GPS”, “magnetometer”, “multibeam sonars”, “IMU”). Furthermore, the terms related to the operational environments such “lake”, “river”, “sea”, “glaciers”, “shallow waters”, “port” and commonly used hull forms such as “catamaran” and “sailboat” appear throughout the term analysis.
Relevant terms within the context of USV applications encompass a wide array of functions and technologies. These terms shed light on how small USVs are utilized. Examples of specific applications and functions include “bathymetry”, “detection”, “mapping”, “survey”, “water quality monitoring”, “temperature”, “monitoring”, “inspection”, “safe and rescue”, “robotic tool”, “remote area” and “jellyfish removal”. These illustrate some of the diverse roles that small USVs have played.
In addition to application-specific terms, the map includes technical terminology required for autonomous or remote operations. Terms like “collision avoidance”, “communication”, “autonomous navigation”, “control system”, “robustness”, “identification”, “localization”, “autopilot”, “estimation”, “identification”, “docking”, “dimensional reconstruction”, “leader” and various control techniques like “model predictive control (MPC)” and “Proportional-Integral-Derivative (PID)” are present. This is unsurprising as these technical aspects’ achievements are fundamental prerequisites for the effective operation of USVs.
The bibliometric analysis using VOSviewer provided only a limited spectrum of answers. To gain a more systematic understanding of USVs use, the research and application types for each of the USVs were identified and presented in Appendix A. In Figure 7, the statistical analysis of these application types is presented with data aggregated manually. It is worth noting that some USVs had multiple roles and utilizations, leading to their inclusion in multiple categories. Therefore, the percentages in Figure 7 do not add up to 100%.
Figure 7 reveals that half of the vessels were primarily employed for the development and testing of novel control techniques or autonomous navigational algorithms. Approximately 18% of small USVs were developed for the purpose of water sampling in lakes, rivers and coastal areas. Another 14% of small USVs were used for bathymetry, while 12% were used for collision avoidance testing. About 11% served for weather and environmental monitoring, excluding water sampling. Additionally, a smaller percentage of USVs were involved in testing novel positioning algorithms, inspecting bridges and other structures, testing hull parameters in the replication of self-propulsion model tests, monitoring animals and invasive species and engaging in search and rescue operations (ranging from 5% to 10% of the investigated USVs in each type of operation).
Furthermore, a few analyzed USVs were deployed in activities like floating garbage cleaning, the development of datasets for object detection training, in testing towing operations and experimenting with the vessel train concept, where a fleet of small USVs was following the leading USV (each accounting for 4% of the investigated USVs). Occasionally, small USVs were used as platforms for developing solutions related to oil spill cleaning, testing swarm operations, or in assessing autonomous navigation in icy conditions. A very limited number of small USVs were utilized for purposes such as cybersecurity research, testing novel risk monitoring algorithms, diagnosing faults, and mapping local areas.
It is important to note that not all of the identified USVs were utilized for subsequent research and development purposes. As illustrated in Figure 8, 59 of the USVs found (70%) were mentioned in just one research paper or website. On the other hand, there was one USV, the WaveGlider, which was extensively used and mentioned in eight papers/references. This discrepancy can be attributed to several factors. It might be due to the limited scope of the present database. It could also reflect the fact that many small USVs were purpose-built as dedicated application platforms, with their primary role implementing a function they were designed for, rather than serving as subjects of extensive research and development.
It is worth noting that only a few of the identified USVs, specifically those described in references [37,84,101,103,121,122,123,124], showcased advanced navigation capabilities in challenging sea-ice-covered environments. However, it is important to acknowledge that some of these USVs may have dimensions that differ from the specific criteria set for the present study (100 kg of displacement), which is why they were not fully analyzed in the database.

3.3. RQ3: The Current Use of Very Small USVs for Safety and Cybersecurity Assurance

Figure 5’s network of authors shows little correlation with the author networks in Figure 7 from [7] and Figure 6 from [15], which focus on autonomous ship safety and maritime cybersecurity, respectively, although some of the researchers are present in at least two out of three. This discrepancy can be attributed to the distinct nature of these domains, each requiring unique skills, research backgrounds and expertise. It is challenging for leading experts to actively engage in research across all three areas simultaneously. This discrepancy highlights the potential lack of significant interconnections between current research on small USVs and safety and cybersecurity.
A deeper bibliometric analysis reveals that terms related to safety presence are not negligible. Keywords such as “safe and rescue,” “reliability,” and “safety” rank among the top 148 keywords according to VOSviewer analysis, appearing in 13% of selected publication titles and abstracts. Terms like “rescue” appear in 7% of references titles and abstracts, “risk” in 4% and “reliability” in 2%. Keywords like “cybersecurity” and “hazard” appear in only 1% of titles and abstracts. In total, 22% of the 183 publications titles and abstracts refer to safety and cybersecurity, indicating that while safety and reliability considerations are not completely overlooked in the small USVs domain, their presence is relatively limited, especially in the case of cybersecurity.
A more systematic analysis reveals that the primary safety-related applications of USVs lie in search (safety) and rescue operations [45,125,126,127,128,129,130,131,132,133], as already pointed out in Section 3.2. Additionally, USVs have been proposed for enhancing safety in various hazardous environments, such as water sampling near glaciers [122,123,134], remote regions [121], areas with wrecks [131] and for safeguarding against environmental threats like cyanobacteria blooms and invasive species [135,136,137]. Small USVs are also employed to identify navigational hazards on river, lake, canal, and sea floors [138,139] and to address oil spill incidents [140,141]. They support safety-related inspections [142], as well.
USVs are also leveraged for enhancing safety in various autonomous operations including tug operations [143,144], docking operations [34,39,145], collision avoidance [32,146] and in the improvement of safety in path following and navigational algorithms [32,142,147,148,149,150,151,152]. Furthermore, USVs are employed to develop risk-aware algorithms for decision-making based on risk [153,154], to ensure the general safety of USVs [155,156] and to develop fault tolerance based applications [45].
Conversely, the utilization of very small USVs in cybersecurity research is quite limited. Only a few instances were found, such as a practical demonstration of hijacking attacks in [157] and the development of ROS-based solutions against transmission cyber-attacks in [158]. This scarcity can be attributed to the emerging nature of the maritime cybersecurity domain and the challenges in scaling up results from small to larger USVs due to the differences in hardware/software (Section 3.2).
In conclusion, the findings indicate that safety- and cybersecurity-related aspects have not been extensively explored within the realm of small USVs. Nonetheless, existing applications demonstrate the potential of USVs to improve operational safety by replacing human involvement in perilous situations, identifying safety-related objects, monitoring environmental safety, and mitigating the impacts of disasters. Moreover, USVs have made significant contributions to enhance the safety of navigation, autonomy, detection, positioning, control algorithms and overall USV safety.

4. Potential Directions for Future Research in USVs Related to Safety

In the next section, the discussion concentrates on potential research directions integrating very small USV use and safety/cybersecurity research. The identified directions are based on the findings related to RQ3 and also the directions for further safety/cybersecurity research in connection to the autonomous ships proposed in [7,8,15]. They are grouped under the categories related to the algorithms’ verification (Section 4.1), sensors’ verification (Section 4.2), hazard identification and risk assessment for larger autonomous ships (Section 4.3), safety assurances of communication systems and cybersecurity enhancement (Section 4.4) and extended applications of small USVs for accident mitigation (Section 4.5).

4.1. Very Small USVs for Safety Assurance of Control, Collision Avoidance and Navigation

Undoubtedly, ensuring the safety of autonomous navigation algorithms constitutes one of the greatest obstacles to a wider adoption of autonomous ships [7,8]. As was observed in Figure 7 and Section 3.3, small USVs are widely used for the development and testing of novel control techniques.
In this way, the results of the control techniques can be a useful way to verify the functionality of the collision avoidance techniques, algorithms or novel control techniques and augment the safety case for a control, navigation and collision avoidance algorithm. However, it is important to acknowledge that the direct extrapolation of these findings from small-scale models to larger vessels is not straightforward. Various factors, such as Reynolds, Cauchy, Froude and geometrical similitude, hull roughness effect, thrust, advance, cavitation and wake coefficients disparities between the autonomous model and autonomous ship wield a substantial influence on the type of resistance proportions and propulsion efficiency encountered by the actual ship in comparison to the small scaled model [28,30,159,160,161,162]. The development of collision avoidance algorithms and vessel train concepts is substantially influenced by collision similitude metrics based on Time to Closest Point of Approach (TCPA) and Distance to the Closest Point of Approach (DCPA) [46], which will require another type of scaling [46].
Consequently, these factors play a pivotal role in shaping the controller settings for speed, rudder, and path-following algorithms if extrapolated to larger vessels. This challenge becomes even more intricate if the controller incorporates machine learning-based control techniques, as the adjusting machine-learning controller might require better explainability between input and output relationships [163]. Some indications of this research being conducted can be found in [46,164], although fairly few details have been released. The careful consideration and investigation of these factors’ impacts on scaling up could constitute an area of interesting research.
Scaling up operations to handle adverse weather conditions presents an additional challenge, given that many USV applications have been demonstrated in calm waters and favorable weather conditions (see Appendix A), with few notable exceptions, as in [39,165,166,167]. Nevertheless, it should be emphasized that autonomous navigation in adverse weather conditions is important in preventing potential accidents [9,168]. It is noteworthy that the formalization of this process for small USVs has not been observed in any of the examined publications, despite expressions of concern in [41].
Testing in adverse weather conditions will require wave generators in tanks, which will increase the cost of testing. Furthermore, the discrepancy in equipment type might result in the need for careful consideration for ensuring that similar GM is achieved in the small USV employed. The problem with stability due to uplifted camera, LiDAR or useful equipment is one of the reasons why so many small USVs were designed with a catamaran or trimaran hull, as observed in Section 3.2. So, this might yield another challenge to be addressed, but probably it will not be a critical one.
Control algorithms scaling up from small USVs to larger vessels present several additional challenges, notably due to the discrepancy in sensors and actuators’ types and quality. It is imperative to account for the disparities in sensor types used on small USVs compared to those employed on actual ships, as was concluded in Section 3.2. This is since the performance of control algorithms can be significantly influenced by sensor quality and the resolution and dynamics of sensor/actuators. Furthermore, on small USVs, space limitations can potentially constrain sensor and actuator resolution, but not essentially. Moreover, small USVs might utilize lower-quality sensors and actuators compared to their larger ship counterparts. It is important to recognize that the dynamics of propulsors may diverge [169], as small USV actuators are generally electrically powered (Section 3.2), which may not hold true for larger ships equipped with internal combustion engines or other propulsion systems.
In addition, the utilization of distinct software and hardware computational tools on ship models, such as ROS-based ones and LabVIEW (Section 3.3), compared to those deployed on actual ships, can introduce another factor that affects the accuracy and performance of control algorithms. Of course, this obstacle can be overcome, with large, unmanned ships running on ROS2, but this might be highly unlikely. So it might be difficult to implement the full software assurance just using USVs, and it will need to complement other state-of-the art approaches for software assurance such as testing for software in the loop, hardware in the loop, etc. [65].
To support safety assertions in collision avoidance, it is imperative to define a dedicated set of collision avoidance test scenarios, as emphasized in previous studies [7,20,24,170]. While COLREGs (International Regulations for Preventing Collisions at Sea) can serve as a foundational source, their application can be further enriched [24]. This is particularly relevant because, compared to larger ships, the cost of testing USVs is significantly reduced. To achieve this, the development of a fleet of USVs becomes essential, enabling the testing of USV interactions with various types of ships and objects, and thereby enhancing comprehensiveness [16,20,21,24,106]. Some indicative research has been demonstrated in [38,42,171]. Furthermore, the diverse roles and functions of USVs can also serve as valuable sources for generating collision avoidance scenarios for testing and validation [172]. This multifaceted approach ensures that collision avoidance algorithms and safety measures are thoroughly evaluated across a spectrum of realistic scenarios and operational contexts.
Research focused on small USVs encompasses very few instances where novel fault-tolerant control and fault-tolerant techniques are investigated and tested [45]. While it may not be possible to directly apply these techniques to larger USVs due to the differences in equipment, it is a useful approach to initially assess their efficacy on small USVs before considering their implementation on larger systems.

4.2. Very Small USVs for the Safety Assurance of Sensory Systems

As evident from the table provided in Appendix A and Section 3.2 results, a notable difference exists between the sensory systems employed by small USVs and those used on larger vessels. Small USVs predominantly rely on GNSS, LiDARs and, in specific cases, cameras for navigation, rarely using radar or AIS commonly present on ships [173]. Consequently, sensor fusion solutions developed for USVs that do not incorporate AIS and radar may not be directly transferrable to larger ships. However, it should be underscored that this is subject to the specifics of each individual case and can be investigated in future research.
It is worth noting that the performance requirements for GNSS on USVs are generally stricter or at least similar to those for typical vessels due to the USVs size. Thus, positioning algorithms that are proved successful through GNSS testing on small USVs can potentially be adopted for larger vessels, so USVs can potentially prove a valuable platform for GNSS novel algorithm development to be applied in larger ships.
Similarly, the effectiveness of positioning algorithms employing cameras or a combination of GNSSs and cameras can be validated on small USVs, as in [39]. This can constitute valuable evidence for implementing similar camera-based or other sensor-based positioning on larger vessels. However, it is important to recognize that the detection ranges of cameras or sensors used on small USVs may need to align with the scaled requirements for larger ships [174].
Nonetheless, small USVs, when equipped with appropriate cameras and sensory systems akin to those on larger autonomous ships, can serve as valuable platforms for aggregating the essential training data for object detection and recognition in a variety of operational environments. Examples of such development initiatives have been demonstrated in [44,175,176,177]. This advancement is vital, as highlighted in [7,178], to address the evolving demands of autonomous navigation and safety.

4.3. Very Small USVs for Hazard Identification and Risk Assessment

The utilization of small USVs for risk identification, as observed in larger USVs [179], has not been widely reported or the reports are very scarce, at least based on the present survey (Section 3.3). This can be attributed to the relatively small scale of risks associated with small USVs, rendering the implementation of risk assessments less cost-effective and, in many cases, unnecessary, or making the publishing of relevant results uninviting. However, there have been reports on the testing of online risk monitoring algorithms in medium size USVs [153,180] or risk-aware control algorithms [154], which holds significance in the context of self-aware autonomous systems [7,181,182]. Pertinent real-time risk assessment algorithms can be preliminarily tested on small USVs before being considered for deployment on larger vessels or USVs, considering the scale factor’s impact on maneuverability.
Furthermore, small USVs can be employed to assess safety performance in specific critical scenarios, as has been reported in [46], akin to what is implemented for larger USVs [183]. These data can contribute to the augmentation of the risk assessment framework for larger USV models, especially in navigation. However, it should be noted that it cannot entirely replace it due to disparities in equipment. The explanation is provided below.
The technical risk sources differ significantly between small and large USVs because of the substantial variations in equipment. Additionally, maintenance risks and management risks vary between these categories of USVs [184]. While the human–machine interaction risks on small USVs may share some similarities with those on larger ships, they will not be identical, primarily due to the aforementioned differences in equipment and operational context [183,185]. Consequently, the risk profiles for small and large USVs are distinct and need to be assessed separately.
However, the disparities in navigational hazards between small and large USVs are considerably reduced. In terms of navigational hazards, the deployment of small USVs equipped with automatic detection and recognition algorithms within the operational areas relevant to larger USVs could serve to identify these hazards and assess their frequency of occurrence. This approach allows small USVs to contribute to the refinement of risk assessments for larger USVs, ultimately reducing uncertainty and improving overall risk assessment quality, overcoming the limitations of AIS data [186] in a similar way as they currently do with the bathymetry data (Section 3.2 and Section 3.3).

4.4. Very Small USVs for the Safety Assurance of Connectivity Systems and Cybersecurity

The majority of the reported USVs have been documented as using WiFi or radio communication systems for remote control, which can be very unsecure [187]. However, in some instances, there have been mentions of 3G/4G communication technologies. It is anticipated that 4G or even 5G will become prevalent in larger USVs in vicinity to the shore [188]. Potentially, it will become feasible to assess the reliability of these communication protocols (4G/5G) or even satellite communications on small USVs [189,190], allowing for the identification of issues that may also be relevant to larger USVs [56].
As has been demonstrated in Section 3.3, there have been very few publications focused on cybersecurity research in connection to the small USVs. Future applications could involve the testing of more advanced cybersecurity control measures, such as intrusion detection systems and novel cryptographic approaches, while continuing to evaluate the impact of cyberattacks on small USVs in accordance with findings from [15] or while investigating the impact of adversarial attacks [191]. The concepts and models developed for small USVs can be of practical benefit for larger USVs, even though the direct applicability may be impeded due to differences in equipment, associated vulnerabilities and software used.

4.5. Very Small USVs for Search and Rescue Disaster Relief Operations

Numerous small USVs have been reported for use in search and rescue operations, as well as in disaster relief efforts, as reported in Section 3.3 and other review studies [56]. While these small USVs may not be directly relevant in the context of safety assurances for autonomous ships, they represent a significant and noteworthy application. The use of rescue and other USVs holds potential in remote and dangerous areas, as they can offer a means of assisting in emergencies and improving response speed in areas such as the Arctic [192] or can replace humans, thus reducing the risks [9].

5. Study Limitations

This article predominantly centers on very small USVs. This choice was made considering that small USVs are often more accessible and cost-effective for educational and research purposes. While extending the research to larger USVs might yield additional insights, it is worth noting that some of the findings presented in this review are likely to be applicable to larger USV types, as well. It is anticipated that the scaling of results from larger USVs to their relevant autonomous counterparts might be easier due to the reduced impact of scale factors.
It is important to recognize that one of the limitations of this study is the exclusion of security and military applications of small USVs. This exclusion may have led to a restricted identification of relevant cybersecurity studies. Consequently, the conclusions drawn from this study with respect to cybersecurity are primarily relevant to civil applications and should be treated with caution.
Furthermore, since the present analysis primarily focused on safety-related aspects and research-oriented publications, it has a limited incorporation of industrial perspectives. This limitation was partially mitigated through Google and OpenAI searches, but most of the examined applications still revolve around academic publications.

6. Conclusions

In this article, the investigation has centered around exploring how very small USVs can contribute to enhancing safety and cybersecurity assurance in civil applications. This exploration was conducted through a comprehensive literature review, bibliometric analysis and investigation of aspects associated with the safety and cybersecurity of autonomous ships.
The primary findings of this study are as follows:
  • Significant ongoing research into very small USVs (those with a displacement of less than 100 kg) is taking place in countries such as the USA, China, South Korea, Portugal, Italy and The Netherlands.
  • Catamaran and trimaran hulls have gained popularity among the very small USV applications.
  • GNSS-based navigation seems to be the predominant option for the positioning of very small USVs, although cameras and LiDARs are also used.
  • Small USVs use has been largely confined to the development of navigation and control techniques. However, other applications include water sampling and analysis, bathymetry use and the testing of collision avoidance techniques and environmental monitoring.
  • The research on very small USVs seems somewhat detached from research on safety and cybersecurity assurance, with no leading experts overlapping with the two areas, although there are indications of connections between these areas.
  • Very small USVs offer a valuable platform for testing and demonstrating the safety and reliability of various algorithms, including those related to positioning, navigation, collision avoidance, leader-following, detection, recognition, fault tolerance, and risk monitoring. However, when applying these algorithms to larger ships, it is essential to consider similitude factors related to hydrodynamics, ice conditions, collision avoidance, hardware and software.
  • Very small USVs can serve as platforms for collecting navigational data and object detection/recognition data, thus reducing uncertainty in assessing risks associated with navigation.
  • Furthermore, very small USVs can play a role in assessing the impact of different attack scenarios on navigational systems, but not in a vulnerability assessment due to different hardware/software. Small USV use may be able to contribute to the development of novel communication protocols, prototype defense systems against cyberattacks, and the evaluation of communication link performance in both shore and remote areas, such as the Arctic.
  • Additionally, small USVs may have applications in search and rescue operations in remote regions, potentially reducing response times and enhancing emergency response capabilities.
It is anticipated that the results of this study will serve as inspiration for researchers in their challenging endeavors, spark ideas for funding organizations, and foster greater interconnection between research areas encompassing safety, control, design, artificial intelligence and mechatronic engineering. Future research could extend to incorporating larger USVs (with displacements ranging from 100 kg to 10 tons) and investigating their utility for safety and cybersecurity.

Author Contributions

Conceptualization, V.B.; methodology, V.B., O.P. and O.A.V.B.; investigation, V.B., T.S. and A.S.; resources, V.B. and T.S.; data curation, V.B. and T.S.; writing—original draft preparation, V.B.; writing—review and editing, A.S., O.P., P.K. and O.A.V.B.; visualization, V.B.; supervision, O.P. and O.A.V.B.; funding acquisition, V.B. and O.A.V.B. All authors have read and agreed to the published version of the manuscript.


The conducted research has been supported by Merenkulunsäätiö research fund number 20220088 and ECAMARIS project funded by Business of Finland under grant number 20200030.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A. Table Including Investigated Small USVs

a/aModel NameYear First MentionedOwnerCountryType and Size
(Length × Beam, Displacement)
Sensory, Software and Propulsion SystemsUtilityOperational AreaRNRef
1Hendrik2019KU LeuvenBelgiumRiver barge
154 cm × 20 cm
32 kg
Raspberry Pi, Navio2, Gyro, LiDAR, GPS, ROS-Control algorithm development
-Model testing
2Yellowfish2022Universidad LoyolaBoliviaCatamaran
24 kg
Raspberry Pi,
Navio2 (GPS, IMU, Radio)
-Testing of estimators
-Estimation of state dynamics
3 2013Instituto Tecnológico de AeronáuticaBrazilCatamaran
120 cm × 120 cm
GPS, thrusters, IMU, digital compasses-Position algorithms improvementLake2[194,195]
4AERO4River2021Federal University of Juiz de ForaBrazilCatamaran
140 cm
20 cm
20.8 kg
Aerial thrusters and servos, GPS, PID control, -Control algorithms development, inspections, sensors placement, ship parameters identificationRiver5[148,152,196,197,198]
5N-Boat2016Federal University of Rio Grande do NorteBrazilSailboat
WiFi, GPS, Wind sensor-Control development using various techniques and their testing, water monitoringLake5[150,199,200,201,202]
6 2021St John’s UniversityCanadaPlatform supply vesselCameras, IMU, propellers, bow and aft thrusters-Development of ice navigation techniquesIce towing tank2[37,151]
7Eddy2021York UniversityCanadaTrimaranGPS RTK, cameras, IMU, ROS, sonar sensor-Infestation and invasive species monitoringLakes1[203]
8WS-USV2014Shenyang Institute of AutomationChinaMonohull
260 cm × 80 cm
70 kg
Rudder with propeller, GNSS, freeway radio-Water sampling
-Detection system development through images
-Model parameters identification
Lakes and rivers4[44,175,176,177]
9Zhi Long N12022Dalian Maritime UniversityChinaTrimaran
175 cm × 50 cm
40 kg
Double propeller and rudder, GPS with RTK, LiDAR, Cameras, ROS C++-SLAM and positioning improvementHarbor area2[204,205]
10Pallas2020Wuhan University of TechnologyChinaInland container vessel
100 cm
GPS, IMU, ROS-Collision avoidance testingRiver2[42,206]
11AquaSentinel2023Ocean AlphaChinaTrimaran hull design
165 cm × 70 cm, 42 kg
Side scan sonars, echo sounders, Doppler sensors, radar sensors, cameras, communication system, waterjets-Hydrographic survey
-Bathymetry survey
-River velocity survey
Harbor areas/sheltered areas1[207]
12Qiuxin No.52023Wuhan University of TechnologyChinaTugboat
227 cm × 65 cm
GPS, gyro-Controller optimization and tuningRiver1[208]
13 2022Shanghai Jiao Tong UniversityChinaCatamaran
200 cm × 119 cm
120 kg
GPS RTK, wave radar, 2 thrusters, ROS-Testing control techniques
-Testing of follow the leader algorithm (vessel train)
14Hong Dong 12023Shanghai Jiao Tong UniversityChina150 cm × 74 cmIMU, GPS, LiDAR-Testing ship detection using LiDARSwimming pool1[143]
15 2020Guangzhou Institute of TechnologyChinaCatamaran 80 cm × 180 cm 5 kgARM microcontroller, satellite communication, photovoltaic, WiFi, water quality analysis device, gyroscope, magnetometer, GPS-Water quality monitoringLake1[210]
16USBV I-II2010State Oceanic AdministrationChinaCatamaran 280 cm × 150 cm 100–130 kgDGPS, IMU, Compass, echo sounder, camera, weather station, propellers-Bathymetry Testing of controllers Sensors testingCoastal area4[211,212,213,214]
17 2009Shanghai Maritime UniversityChinaCatamaran 270 cm × 148 cm 100 kgTwo propellers, LAN, NMEA, cameras, GPS-Surveillance, quality sampling, hydrological survey, search and rescueCoastal area1[132]
18USCV2020Guangxi UniversityChinaCatamaran 133 cm × 95 cm 50 kgRemote control system-Garbage cleaning, system developmentHarbors, rivers1[215]
19 2021Zhejiang University of Water Resources and Electric PowerChinaUndefinedGPS, LiDAR, Camera, WiFi, IMU, ROS, radio communication, thrusters-Garbage cleaning, system development, autonomous navigation developmentHarbor, rivers1[146]
20 2016Universidad Tecnologica de BolivarColombiaMonohull 130 cm 17 kgRaspberry Pi, GPS, IMU, radio, Matlab, simulinki, Navio+-Control testing, environment monitoringlakes2[216,217]
21PlaDyPos/PlaDyBath2015University of ZagrebCroatiaNew type
35 cm × 35 cm
25 kg
GPS, Doppler speed sensor, compass, IMU-Control development and verification
-Scanning the sea bed
Lakes, sea4[140,218,219,220]
22ARCAB2019Aarhus UniversityDenmarkTrimaran
80 cm × 100 cm
GNSS receivers,
FPV camera
-Assessment of climate change impact
-Collection of surface water samples in hazardous area
Sea, close to melting icebergs1[122]
23NORDACC2023Aarhus UniversityDenmarkTrimaran
93 cm× ?
GNSS receivers,
FPV camera
-Assessment of climate change impact
-Collection of surface water samples in hazardous area
Marine bay, melting iceberg1[123]
24AL2023Aalto UniversityFinlandIcebreaker
135 cm × 38 cm
20 kg
LiDAR, GPS, IMU, WiFi, RP4, Arduino Mega, 3 Azimuth thrustersResearchIce and wave towing tank1[147]
25ROSS2007National Institute of OceanographyIndia184 cm × 36 cm
108 kg
RF, GPS, 2 BLDC motorsOcean remote sensingOpen ocean1[221]
26BAICal2022LASA, University of CalabriaItalyFour buoys connected together
10 kg
GPS with RTK,
Azimuth thrusters,
IMU, Raspberry Pi, Python, ROS
-Collection of environmental data
-Remote web application testing
-Navigation system development
-Fault diagnosis and control development
Lake, close to sea shore1[45]
27SWAMP2020University of GenovaItalyCatamaran
123 cm × 110 cm
58 kg
IMU, GPS, WiFi, Arduino, Raspberry Pi-Monitoring close to glaciers
-Water sampling
-Landing/take off platform
-Power management development
Shallow water operations4[155,222,223,224]
28MicroVega2015University “Parthenope” NapoliItalyCatamaran 135 cm × 85 cm 14 kgSONAR, IMU, GPS, 2 motors, camera, underwater camera, Linux, Arduino, RTK, WiFi, Tritech StarFish and TrackStar, Arduino Mega, Raspberry Pi, C++-Bathymetric data acquisition, collision avoidance testingLakes, close to coast4[225,226,227,228]
29Shark USSV2016Institute for coastal marine environmentItalyUndefined 90 cm × 75 cm 40 kgFour propellers Linux, GPS, AHRS, WiFi, camera, C++-Water sampling in proximity to glaciers Towing operationsGlaciers2[134,229]
30WeMo2020University of SienaItalyUndefined 12.7 kgArduino Uno, GPS, sonar, pH, oxidation-reduction, salinity, oxygen, flow rate, sonar, sensors-Environmental monitoring, navigation controlRiver2[230,231]
31 2019Tokai UniversityJapanCatamaran
88 cm × 35 cm
Very little information-Position estimation, collision avoidance, garbage recognition, position detection algorithm development and testingPool1[232]
32 2014KAISTKoreaTrimaran
2.8 m × 1.5 m
80 kg
LiDAR, cameras, GPS, WLAN, PC modules, trolling thruster system-Development of navigation and mapping algorithms
-Testing of geophysical navigation
180 cm × 90 cm
60 kg
IMU, GPS, 2D LiDAR, 3D LiDAR, heading reference system,-New positioning system developmentClose to offshore structures1[235]
34JEROS2012KAISTKoreaCatamaran150 cm × 110 cm 50 kgGPS, IMU, 2 thrusters, cameras-Jellyfish removal
-Path planning algorithm testing
-Formation following algorithm, jellyfish detection
Coastal area5[236,237,238,239,240]
35 2016KAISTKoreaCatamaran 100 cm × 25 cmPropeller, rudder, camera, GPS, IMU, LiDAR-Bridge inspectionRivers, lake1[241]
36 2022Inha UniversityKoreaCatamaran
144 cm × 77 cm
GPS, Arduino, ROS, Python, LiDAR, Raspberry Pi-Collision avoidance developmentTowing tank1[38]
37PASS Mk II2023Pukyong National UniversityKoreaCatamaran
120 cm × 60 cm
15 kg
GPS with RTK,
IMU, Raspberry Pi, Arduino, Azipods
-Control algorithms development and testingTowing tank1[34]
38 2023Pukyong National UniversityKoreaMonohull
200 cm × 49 cm
Uknown-Testing of control algorithm with gain tuning using free running test dataLake1[242]
39 2016IIUMMalaysiaCatamaran
100 cm × 92 cm
ArduPilot, telemetry, GPS, compass, sonar sensor-BathymetryLake1[138]
40UNIGE2020TuDelftThe NetherlandsTugboat
97 cm × 30 cm
-IMU, GPS, ultrasonic sensors, azimuth thrusters-Ship control algorithm development
-Collision avoidance testing
Towing tank1[42]
41Tito-Neri2020TuDelftThe NetherlandsTugboat
145 cm
16.9 kg
Accelerometers, distance measurement sensors, gyro, GPS, encoders, camera, ROS, Python, Arduino-Ship control algorithm development
-Testing of follow-the-leader algorithm (vessel train)
-Testing towing operations
Towing tank, lakes, rivers5[42,144,243,244,245]
42Grey Seabax2021TuDelftThe NetherlandsOffshore ship
175 cm
19 kg
Accelerometers, distance measurement sensors, gyro, GPS, encoders, camera, ROS, Python, Arduino-Ship control algorithm developmentTowing tank1[243]
43Delfa 1 2021TuDelftThe NetherlandsCatamaran
5 kg
Gyro, GPS, Cameras-Ship control algorithm developmentTowing tank1[243]
44Roboat2018RoboatThe NetherlandsUrban Ferry
90 cm × 45 cm
9 kg
ROS, RTK GPS, IMU, LiDAR-Navigation system design
-Testing of control techniques
-Testing of leader-follower algorithm
45Otter2021Marine RoboticsNorwayCatamaran
200 cm × 108 cm
GNSS, IMU, Stereo cameras, WiMax radio, LiDAR-Control algorithms development and testing
-Position, speed algorithms improvement and testing
-Visual based algorithms
-Injecting cyberattack scenarios to navigation system
-Development of encryption algorithms
-Online risk monitoring testing
258 cm × 44 cm
Arduino, IMU, WiFi bridge, Qualisys, LabVIEW-Hydrodynamic experiments
-Wave parameters estimation
Towing tank2[250,251]
47SailBuoy2014MET NorwayNorwayMonohull
2 m
60 kg
Satellite communications, GPS, Temperature, Oxygen sensor-Sea water monitoringGulf of Mexico1[252]
48 2016NTNUNorwayHigh speed boatThrusters, dynamic positioning, GPS, IMU, Linux, Arduino, temperature, pressure, humidity sensorsInspection of aquafarms, control developmentClose to aqua farms1[253]
49Cybership II2004NTNUNorwaySupply ship
125 cm × 29 cm
24 kg
LabVIEW, WLAN-Ship parameters identification
- Formation maneuvering testing
50 2019Sultan Qaboos UniversityOmanCatamaran 90 cm 15 kgRaspberry Pi, ROS, GPS, IMU, cameras, oil sampling mechanism-Navigational algorithm for oil spill responseSea1[141]
51 2017Pontificia Universidad Catolica del Peru San MiguelPeruCatamaran 130 cm × 90 cm 50 kgGPS, WiFi, bathymeter, sampling device, IMU, Raspberry Pi 3, camera, radio -Bathymetry, task allocation algorithms testingLake2[256,257]
52 2017Gdynia Maritime UniversityPolandCatamaran (dimensions unknown but seemingly small)Echosounder, GPS RTK, 2 propellers, Pixhawk, ATmega8 -Hydrographic surveyLakes, sea2[139,258]
53ROAZ2013INESC TECPortugalHigh speed boat
90 kg
WiFi communication,
-Search and rescue operations
-Positioning algorithm testing
Coastal area4[125,126,127,128]
54 2021CENTECPortugalContainership
324 cm × 43 cm
108 kg
LabView, GPS, IMU, rudder, propeller-Model parameters estimation
-Collision avoidance testing with other ships
55 2021CENTECPortugalChemical tanker
258 cm × 43 cm
LabView, GPS, IMU, rudder, propeller, WiFi, wind sensor-Model parameters estimation
-Shallow water effect investigation
-Collision avoidance testing
-Collision avoidance testing with other ships
56Zarco2007University of Porto PortugalCatamaran, sailboat
150 cm
50 kg
WiFi, GPS, Compass, C++, Linux-Research, underwater surveys, station keeping algorithms development, wind propulsion testing, bathymetry data, sonar technology developmentRivers8[129,262,263,264,265,266,267,268]
57FEUP (FASt)2008University of PortoPortugalSailboat
250 cm × 67 cm
50 kg
Linux, WiFi, modems, wind vane, anemometer, radiocommunications, compass, GPS, inclinometers, voltage, light temperature, moisture sensors, ANSI Solar panels-Ocean observation, coastal surveillance, reconfiguration testing, speed controller testing, navigation controller testingCoastal area6[156,165,269,270,271,272]
58UCAP2013University of PortoPortugalMonohull (high speed)
90 kg
WiFi, GPS, IMU, PID controllers, -Search and rescue operationsCoastal area1[125]
59 2018Instituto de TelecomunicaçõesPortugalHigh speed boatROS, Linux, pH, water temperature, salinity, depth, turbidity, conductivity sensors, IMU, GPS, Camera, Raspberry Pi, 2 thrusters, Bluetooth-Communication networks testing and development
-Swarm algorithm development
60ELFIN2023Weston RobotSingaporeCatamaran
100 cm × 75 cm
-Water sampling with remote or automatic controlHarbor1[277]
61A-Tirma G1 and G22014Instituto Universitario SIANISpainSailboat
1 m × 0.25 m
4.3 kg
2 m × 367 cm
42 kg
RF, GPS, Compass, Wind, Inclinometers, C++-Research, fish monitoring, design optimizationCoastal area5[278,279,280,281,282]
62 2015Universidad Complutense de MadridSpainHighspeed boat 0.8 m–1 m 3.4–3.9 kgARM microcontroller, GPS, radio link, compass, C++-Towing operation testing, navigational control, oil cleaning operations, buoys deployment operationsLake4[283,284,285,286]
63 2021Universidad Complutense de MadridSpainCatamaran 10 kgRadio link, GPS, IMU, compass, temperature, pH, conductivity-Water monitoringLake1[135]
64Deep Vision2022KTHSwedenCatamaran
(dimensions unknown, but small as judged from the pictures)
Wireless radio, IMU, GNSS RTK, sonar, Arduino mega, AIS-ResearchClose to coast1[287]
65 2019National Sun Yet-san UniversityTaiwanKayak, 363 cm × 91 cm 88.5 kgGPS, LiDAR, camera, Pixhawk, radio communication-Autonomous sailing, remote communications, smart 3D mapping, real-time image detection and identificationCoastal area1[288]
66 2021Naval AcademyTunisiaMonohull
314 cm
85 cm
Sonar, weather vane, anemometer, GPS, Video, IMU, siren, LiDAR, Arduino-Control algorithm developmentCoastal area2[289,290]
67 2006University of WalesUKSailboat
1.5 m
GPS, compass, wind-Research, monitoring, control techniques developmentLake3[291,292,293]
68 2018University of LeedsUKTrimaran 56 cm × 45 cm GPS, remote control, sonar-Bathymetry close to glaciersLakes1[294]
69 2017University of BathUKCatamaranManual control, GPS, ROS, IMU, satellite communication, IMU, optical camera, sonars, Linux-Bathymetry, navigation and guidance testing, objects detectionRiver1[295]
70Wave Glider2010Liquid RoboticsUSAGlider type
Comparable to paddle board
Solar panel, battery, AIS, GPS, speed and customized sensors, gliding system-Oceanographic research
-Environmental monitoring (fish, tsunamis, met ocean, hydrocarbon)
-Mammals and acoustic monitoring
Open ocean9[154,167,296,297,298,299,300,301,302]
71sUSV2019University of Southern MississippiUSABoard
100 cm
ArduPilot, cameras, GPS-Data collection platform at the coral reefsNear coastline1[303]
72Sea-RAI2009University of South FloridaUSACatamaran
190 cm × 120 cm
Acoustic cameras, GPSs, video cameras-Inspection in the aftermath of hurricanesRivers2[130,131]
73BathyBoat2010University of MichiganUSAHigh speed boat 97 cm
16 kg
GPS, IMU, Sonar, rudder, radio communication-Bathymetry and fish findingLakes1[121]
74AutoCat2000Massachusetts Institute of TechnologyUSACatamaran
180 cm × 130 cm
Two motors, two Astroflight motor controllers-ResearchRivers1[304]
75 2020Washington State UniversityUSAHydrofoil monohull and Trimaran
61 cm × 16.5 cm
65 cm × 41 cm
Cameras, GPS, radios, CAN bus, Arduino Mega-Research Lake1[305]
76SCOUT2005Massachusetts Institute of TechnologyUSAKayak
3 m
82 kg
GPS, compass, WiFi, RF modem-Research
-Sampling platform
Coastal area2[306,307]
77Smart Emily (Emily)2013Texas A&M UniversityUSABoard,
10 kg
GPS, remote control, android application-Search and rescue operationsCoastal area2[47,133]
78CRW2012Carnegie Mellon UniversityUSA40-70 cmWiFi, 3G, Arduino mega, sonars, fluorometer, gyro, camera, IMU, GPS-Water quality monitoring, depth buoy verification, flood disaster mitigation
-Collision avoidance testing using smartphones
-Fleet control development
Lakes, canals3[308,309,310]
79MARV2016Santa Clara UniversityUSACatamaran,
106 cm × 60 cm
25 kg
WiFi, GPS, Sonar-ResearchLakes, ponds1[311]
80USNA sailboat2010United States Naval AcademyUSASailboat
2 m × 0.3 m
30 kg
WiFi, GPS, Compass, Wind-Competition, navigation, power management, collision avoidance developmentCoastal area3[312,313,314]
81Kingfisher2016Clear Path RoboticsUSACatamaran 135 cm × 98 cm 28 kgLinux, WiFi, GPS, remote control, 2 thrusters, vacuum system, flow rate calculator, water sampling sensors-Water samplingLakes3[137,166,315]
82 2009 USACatamaran 2 m × 1 m 100 kgTemperature, salinity, conductivity, salinity, turbidity, solar panels, wireless communication -Water samplingRiver2[316,317]
83SMARTBoat 52019SMART LabUSAHovercraft 104 cm × 99 cmROS, camera, GPS, IMU, duct fans-Cleaning from garbage River lake1[136]
84VIAM-USV20002021Ho Chi Minh City University of TechnologyVietnamCatamaran Seemingly smallROS, GPS, LiDAR, WiFi, C++-Path following, obstacle avoidanceLake2[318,319]


  1. Negenborn, R.R.; Goerlandt, F.; Johansen, T.A.; Slaets, P.; Valdez Banda, O.A.; Vanelslander, T.; Ventikos, N.P. Autonomous ships are on the horizon: Here’s what we need to know. Nature 2023, 615, 30–33. [Google Scholar] [CrossRef] [PubMed]
  2. Yara. MV Yara Birkeland. Available online: (accessed on 6 December 2023).
  3. Rolls-Royce. AAWA Project Introduces the Project’s First Commercial Ship Operators. Available online: (accessed on 6 December 2023).
  4. Bolbot, V.; Theotokatos, G.; Boulougouris, E.; Wennersberg, L.; Nordahl, H.; Rødseth, Ø.J.; Faivre, J.; Colella, M.M. Paving the way toward autonomous shipping development for European Waters–The AUTOSHIP project. In Proceedings of the Autonomous Ships, London, UK, 17–18 June 2020. [Google Scholar]
  5. Mayflower 400. It’s Time for the Mayflower Autonomous Ship. Available online: (accessed on 11 September 2023).
  6. Choi, J.-H.; Jang, J.-Y.; Woo, J. A Review of Autonomous Tugboat Operations for Efficient and Safe Ship Berthing. J. Mar. Sci. Eng. 2023, 11, 1155. [Google Scholar] [CrossRef]
  7. Chaal, M.; Ren, X.; BahooToroody, A.; Basnet, S.; Bolbot, V.; Banda, O.A.V.; Van Gelder, P. Research on risk, safety, and reliability of autonomous ships: A bibliometric review. Saf. Sci. 2023, 167, 106256. [Google Scholar] [CrossRef]
  8. Li, Z.; Zhang, D.; Han, B.; Wan, C. Risk and reliability analysis for maritime autonomous surface ship: A bibliometric review of literature from 2015 to 2022. Accid. Anal. Prev. 2023, 187, 107090. [Google Scholar] [CrossRef] [PubMed]
  9. De Vos, J.; Hekkenberg, R.G.; Banda, O.A.V. The impact of autonomous ships on safety at sea—A statistical analysis. Reliab. Eng. Syst. Saf. 2021, 210, 107558. [Google Scholar] [CrossRef]
  10. Wróbel, K.; Montewka, J.; Kujala, P. Towards the assessment of potential impact of unmanned vessels on maritime transportation safety. Reliab. Eng. Syst. Saf. 2017, 165, 155–169. [Google Scholar] [CrossRef]
  11. Rødseth, Ø.; Burmeister, H.-C. New Ship Designs for Autonomous Vessels; MUNIN Project: Hamburg, Germany, 2015; p. 36. [Google Scholar]
  12. Kim, T.-E.; Sharma, A.; Gausdal, A.H.; Chae, C.-J. Impact of automation technology on gender parity in maritime industry. WMU J. Marit. Aff. 2019, 18, 579–593. [Google Scholar] [CrossRef]
  13. Bergström, M. Autonomous in the Arctic—Fortune or Folly? Available online: (accessed on 22 September 2023).
  14. Lee, S.-W.; Jo, J.; Kim, S. Leveraging the 4th Industrial Revolution Technology for Sustainable Development of the Northern Sea Route (NSR)—The Case Study of Autonomous Vessel. Sustainability 2021, 13, 8211. [Google Scholar] [CrossRef]
  15. Bolbot, V.; Kulkarni, K.; Brunou, P.; Banda, O.V.; Musharraf, M. Developments and research directions in maritime cybersecurity: A systematic literature review and bibliometric analysis. Int. J. Crit. Infrastruct. Prot. 2022, 39, 100571. [Google Scholar] [CrossRef]
  16. Pedersen, T.A.; Glomsrud, J.A.; Ruud, E.-L.; Simonsen, A.; Sandrib, J.; Eriksen, B.-O.H. Towards simulation-based verification of autonomous navigation systems. Saf. Sci. 2020, 129, 104799. [Google Scholar] [CrossRef]
  17. Yang, X.; Utne, I.B.; Sandøy, S.S.; Ramos, M.A.; Rokseth, B. A systems-theoretic approach to hazard identification of marine systems with dynamic autonomy. Ocean. Eng. 2020, 217, 107930. [Google Scholar] [CrossRef]
  18. Huang, Y.; Chen, L.; Chen, P.; Negenborn, R.R.; Van Gelder, P. Ship collision avoidance methods: State-of-the-art. Saf. Sci. 2020, 121, 451–473. [Google Scholar] [CrossRef]
  19. Lee, P.; Theotokatos, G.; Boulougouris, E.; Bolbot, V. Risk-informed collision avoidance system design for maritime autonomous surface ships. Ocean. Eng. 2023, 279, 113750. [Google Scholar] [CrossRef]
  20. Bolbot, V.; Gkerekos, C.; Theotokatos, G.; Boulougouris, E. Automatic traffic scenarios generation for autonomous ships collision avoidance system testing. Ocean. Eng. 2022, 254, 111309. [Google Scholar] [CrossRef]
  21. Torben, T.R.; Glomsrud, J.A.; Pedersen, T.A.; Utne, I.B.; Sørensen, A.J. Automatic simulation-based testing of autonomous ships using Gaussian processes and temporal logic. Proc. Inst. Mech. Eng. Part O J. Risk Reliab. 2022, 237, 293–313. [Google Scholar] [CrossRef]
  22. INCOSE. Systems Engineering Handbook: A Guide for System Life Cycle Processes and Activities, 4th ed.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2015. [Google Scholar]
  23. Stoumpos, S.; Bolbot, V.; Theotokatos, G.; Boulougouris, E. Safety performance assessment of a marine dual fuel engine by integrating failure mode, effects and criticality analysis with simulation tools. Proc. Inst. Mech. Eng. Part M J. Eng. Marit. Environ. 2022, 236, 376–393. [Google Scholar] [CrossRef]
  24. Guo, S.; Bolbot, V.; BahooToroody, A.; Banda, O.A.V.; Siow, C.L. Identification of hazardous encounter scenarios using AIS data for collision avoidance system testing. In Proceedings of the Advances in the Collision and Grounding of Ships and Offshore Structures, Nantes, France, 11–13 September 2023; pp. 43–50. [Google Scholar]
  25. Brown, D.K. The Way of a Ship in the Midst of the Sea: The Life and Work of William Froude; Periscope Publishing Ltd.: Pittsburgh, PA, USA, 2006. [Google Scholar]
  26. Kashteljan, V. Ice Resistance to Motion of a Ship; Sudostroenie: Leningrad, Russia, 1968. [Google Scholar]
  27. MEPC, R. 2013 Guidelines for Calculation of Reference Lines for Use with the Energy Efficiency Design Index (EEDI). Annex 14. 2013. Available online: (accessed on 22 September 2023).
  28. ITTC. ITTC Quality System Manual—Recommended Procedures and Guidelines. 7.6-01-01. ITTC: Zürich, Switzerland, 2021. [Google Scholar]
  29. ITTC. Recommended Procedures and Guidelines. Procedure, Test Methods for Model Ice Properties. 7.5-02-04-02. 2014; ITTC: Zürich, Switzerland.
  30. ITTC. Recommended Procedures and Guidelines. General Guideline and Introduction to Ice Model Testing. 7.5-02-04-01. 2021; ITTC: Zürich, Switzerland.
  31. ITTC. Recommended Procedures and Guidelines. Procedure, Resistance Tests in Ice. 7.5-02-04-02.1. 2017; ITTC: Zürich, Switzerland.
  32. Xu, H.; Hinostroza, M.A.; Soares, C.G. Modified Vector Field Path-Following Control System for an underactuated Autonomous surface ship model in the presence of static obstacles. J. Mar. Sci. Eng. 2021, 9, 652. [Google Scholar] [CrossRef]
  33. Morel, T.A.; Manzano, J.M.; Bejarano, G.; Orihuela Espina, D.L. Modelling and Identification of an Autonomous Surface Vehicle: Technical Report. 2022. Available online: (accessed on 22 September 2023).
  34. Kim, S.-R.; Jo, H.-J.; Kim, J.-H.; Park, J.-Y. Development of an autonomous docking system for autonomous surface vehicles based on symbol recognition. Ocean. Eng. 2023, 283, 114753. [Google Scholar] [CrossRef]
  35. Zhao, L.; Bai, Y.; Paik, J.K. Global path planning and waypoint following for heterogeneous unmanned surface vehicles assisting inland water monitoring. J. Ocean. Eng. Sci. 2023; in press. [Google Scholar] [CrossRef]
  36. Li, S.; Xu, C.; Liu, J.; Han, B. Data-driven docking control of autonomous double-ended ferries based on iterative learning model predictive control. Ocean. Eng. 2023, 273, 113994. [Google Scholar] [CrossRef]
  37. Murrant, K.; Gash, R.; Mills, J. Dynamic Path Following in Ice-covered Waters with an Autonomous Surface Ship Model. In Proceedings of the OCEANS 2021, San Diego, CA, USA, 20–23 September 2021; pp. 1–4. [Google Scholar]
  38. Kim, J.-S.; Lee, D.-H.; Kim, D.-W.; Park, H.; Paik, K.-J.; Kim, S. A numerical and experimental study on the obstacle collision avoidance system using a 2D LiDAR sensor for an autonomous surface vehicle. Ocean. Eng. 2022, 257, 111508. [Google Scholar] [CrossRef]
  39. Volden, Ø.; Stahl, A.; Fossen, T.I. Development and Experimental Validation of Visual-Inertial Navigation for Auto-Docking of Unmanned Surface Vehicles. IEEE Access, 2023; in press. [Google Scholar] [CrossRef]
  40. Perera, L.; Moreira, L.; Santos, F.; Ferrari, V.; Sutulo, S.; Soares, C.G. A navigation and control platform for real-time manoeuvring of autonomous ship models. IFAC Proc. Vol. 2012, 45, 465–470. [Google Scholar] [CrossRef]
  41. Shen, H.; Wen, G.; Lv, Y. Collaborative Parameter Estimation of Multiple Unmanned Surface Vessels: A Robust Distributed Estimator-Based Approach. IEEE Trans. Ind. Inform. 2023; in press. [Google Scholar] [CrossRef]
  42. Singh, Y.; Slaets, P.; Afzal, M.R. The collaborative autonomous shipping experiment (case): Motivations, theory, infrastructure, and experimental challenges. In Proceedings of the The International Ship Control Systems Symposium, Delft, The Netherlands, 6–8 October 2020. [Google Scholar]
  43. Goudey, C.A.; Consi, T.; Manley, J.E.; Graham, M.M. A robotic boat for autonomous fish tracking. Mar. Technol. Society. Mar. Technol. Soc. J. 1998, 32, 47. [Google Scholar]
  44. Zhang, M.; Li, D.; Xiong, J.; He, Y. Multi-dimensional water sampling unmanned surface vehicle development and application. Int. J. Dyn. Control. 2023, 11, 3188–3208. [Google Scholar] [CrossRef]
  45. D’Angelo, V.; Folino, P.; Lupia, M.; Gagliardi, G.; Cario, G.; Gaccio, F.C.; Casavola, A. A ROS-Based GNC Architecture for Autonomous Surface Vehicle Based on a New Multimission Management Paradigm. Drones 2022, 6, 382. [Google Scholar] [CrossRef]
  46. Ten Hoven, D.; Friedhoff, B.; Roettig, F.; Lachmeyer, A.; Lutz, A. Deliverable 3.5 Demonstrator Results. 2021. Available online: (accessed on 22 September 2023).
  47. Patterson, M.C.L.; Mulligan, A.; Boiteux, F. Safety and security applications for micro-unmanned surface vessels. In Proceedings of the 2013 OCEANS, San Diego, CA, USA, 23–27 September 2013; pp. 1–6. [Google Scholar]
  48. Manley, J.E. Unmanned surface vehicles, 15 years of development. In Proceedings of the OCEANS 2008, Quebec City, QC, Canada, 15–18 September 2008; pp. 1–4. [Google Scholar]
  49. Bertram, V. Unmanned Surface Vehicles—A Survey; Skibsteknisk Selskab: Copenhagen, Denmark, 2008; Volume 1, pp. 1–14. [Google Scholar]
  50. Corfield, S.; Young, J. Unmanned surface vehicles-game changing technology for naval operations. IEEE Control. Eng. Ser. 2006, 69, 311. [Google Scholar] [CrossRef]
  51. Martin, A.Y. Unmanned maritime vehicles: Technology evolution and implications. Mar. Technol. Soc. J. 2013, 47, 72–83. [Google Scholar] [CrossRef]
  52. Yan, R.-j.; Pang, S.; Sun, H.-b.; Pang, Y.-j. Development and missions of unmanned surface vehicle. J. Mar. Sci. Appl. 2010, 9, 451–457. [Google Scholar] [CrossRef]
  53. Liu, Z.; Zhang, Y.; Yu, X.; Yuan, C. Unmanned surface vehicles: An overview of developments and challenges. Annu. Rev. Control. 2016, 41, 71–93. [Google Scholar] [CrossRef]
  54. Bai, X.; Li, B.; Xu, X.; Xiao, Y. A Review of Current Research and Advances in Unmanned Surface Vehicles. J. Mar. Sci. Appl. 2022, 21, 47–58. [Google Scholar] [CrossRef]
  55. Schiaretti, M.; Chen, L.; Negenborn, R.R. Survey on autonomous surface vessels: Part II-categorization of 60 prototypes and future applications. In Proceedings of the Computational Logistics: 8th International Conference, ICCL 2017, Southampton, UK, 18–20 October 2017; Proceedings 8. pp. 234–252. [Google Scholar]
  56. Jorge, V.A.; Granada, R.; Maidana, R.G.; Jurak, D.A.; Heck, G.; Negreiros, A.P.; Dos Santos, D.H.; Gonçalves, L.M.; Amory, A.M. A survey on unmanned surface vehicles for disaster robotics: Main challenges and directions. Sensors 2019, 19, 702. [Google Scholar] [CrossRef] [PubMed]
  57. Patterson, R.G.; Lawson, E.; Udyawer, V.; Brassington, G.B.; Groom, R.A.; Campbell, H.A. Uncrewed Surface Vessel Technological Diffusion Depends on Cross-Sectoral Investment in Open-Ocean Archetypes: A Systematic Review of USV Applications and Drivers. Front. Mar. Sci. 2022, 8, 736984. [Google Scholar] [CrossRef]
  58. Real-Arce, D.A.; Barrera, C.; Hernández, J.; Llinás, O. Ocean surface vehicles for maritime security applications (The PERSEUS project). In Proceedings of the OCEANS 2015, Genova, Italy, 18–21 May 2015; pp. 1–4. [Google Scholar]
  59. Graham, M.M. Unmanned Surface Vehicles: An Operational Commander’s Tool for Maritime Security; Naval War Coll Newport, Ri Joint Military Operations Dept: Newport, RI, USA, 2008. [Google Scholar]
  60. Rowley, J. Autonomous unmanned surface vehicles (usv): A paradigm shift for harbor security and underwater bathymetric imaging. In Proceedings of the OCEANS 2018 MTS/IEEE, Charleston, SC, USA, 22–25 October 2018; pp. 1–6. [Google Scholar]
  61. Motwani, A. A Survey of Uninhabited Surface Vehicles; Marine and Industrial Dynamic Analysis School of Marine Science and Engineering, Plymouth University: Plymouth, UK, 2012. [Google Scholar]
  62. OpenAI. Can You Refer to Small USVs Examples? Available online: (accessed on 1 October 2023).
  63. Liberati, A.; Altman, D.G.; Tetzlaff, J.; Mulrow, C.; Gøtzsche, P.C.; Ioannidis, J.P.; Clarke, M.; Devereaux, P.J.; Kleijnen, J.; Moher, D. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration. J. Clin. Epidemiol. 2009, 62, e1–e34. [Google Scholar] [CrossRef] [PubMed]
  64. VOSviewer. VOSviewer Visualizing Scientific Landscapes. Available online: (accessed on 1 February 2022).
  65. Bolbot, V.; Theotokatos, G.; Bujorianu, L.M.; Boulougouris, E.; Vassalos, D. Vulnerabilities and safety assurance methods in Cyber-Physical Systems: A comprehensive review. Reliab. Eng. Syst. Saf. 2019, 182, 179–193. [Google Scholar] [CrossRef]
  66. L3HARRIS. C-WORKER 7 Autonomous Surface Vehicle (ASV). Available online: (accessed on 15 October 2023).
  67. ECA Group. INSPECTOR 125/USV/Unmanned Surface Vehicle. Available online: (accessed on 19 September 2023).
  68. Verge, T. The US Navy’s New Autonomous Warship Is called the Sea Hunter. Available online: (accessed on 19 September 2023).
  69. AutoNaut. Wave Propelled USVs. Available online: (accessed on 19 September 2023).
  70. Szelangiewicz, T.; Żelazny, K.; Antosik, A.; Szelangiewicz, M. Application of Measurement Sensors and Navigation Devices in Experimental Research of the Computer System for the Control of an Unmanned Ship Model. Sensors 2021, 21, 1312. [Google Scholar] [CrossRef]
  71. Weston Robot. Unmanned Surface Vessel SMURF. Available online: (accessed on 3 September 2023).
  72. Ziegwied, A.T.; Dobbin, V.; Dyer, S.; Pierpoint, C.; Sidorovskaia, N. Using autonomous surface vehicles for Passive Acoustic Monitoring (PAM). In Proceedings of the OCEANS 2016 MTS/IEEE, Monterey, CA, USA, 19–23 September 2016; pp. 1–5. [Google Scholar]
  73. Siddle, E.; Heywood, K.J.; Webber, B.G.; Bromley, P. First measurements of ocean and atmosphere in the T ropical N orth A tlantic using C aravela, a novel uncrewed surface vessel. Weather 2021, 76, 200–204. [Google Scholar] [CrossRef]
  74. Sutton, A.J.; Williams, N.L.; Tilbrook, B. Constraining Southern Ocean CO2 flux uncertainty using uncrewed surface vehicle observations. Geophys. Res. Lett. 2021, 48, e2020GL091748. [Google Scholar] [CrossRef]
  75. Ferreira, H.; Martins, R.; Marques, E.; Pinto, J.; Martins, A.; Almeida, J.M.; Sousa, J.; Silva, E. Swordfish: An autonomous surface vehicle for network centric operations. In Proceedings of the Oceans 2007-Europe, Aberdeen, UK, 18–21 June 2007; pp. 1–6. [Google Scholar]
  76. Zhang, Y.; Rueda, C.; Kieft, B.; Ryan, J.P.; Wahl, C.; O’Reilly, T.C.; Maughan, T.; Chavez, F.P. Autonomous tracking of an oceanic thermal front by a Wave Glider. J. Field Robot. 2019, 36, 940–954. [Google Scholar] [CrossRef]
  77. Norgren, P.; Ludvigsen, M.; Ingebretsen, T.; Hovstein, V.E. Tracking and remote monitoring of an autonomous underwater vehicle using an unmanned surface vehicle in the Trondheim fjord. In Proceedings of the OCEANS 2015-MTS/IEEE, Washington, DC, USA, 19–22 October 2015; pp. 1–6. [Google Scholar]
  78. Ferreira, H.; Almeida, C.; Martins, A.; Almeida, J.M.; Dias, N.; Dias, A.; Silva, E. Autonomous bathymetry for risk assessment with ROAZ robotic surface vehicle. In Proceedings of the Oceans 2009-Europe, Bremen, Germany, 11–14 May 2009; pp. 1–6. [Google Scholar]
  79. Zwolak, K.; Simpson, B.; Anderson, B.; Bazhenova, E.; Falconer, R.; Kearns, T.; Minami, H.; Roperez, J.; Rosedee, A.; Sade, H. An unmanned seafloor mapping system: The concept of an AUV integrated with the newly designed USV SEA-KIT. In Proceedings of the OCEANS 2017, Aberdeen, Scotland, 19–22 June 2017; pp. 1–6. [Google Scholar]
  80. Johnston, P.; Poole, M. Marine surveillance capabilities of the AutoNaut wave-propelled unmanned surface vessel (USV). In Proceedings of the OCEANS 2017, Aberdeen, Scotland, 19–22 June 2017; pp. 1–46. [Google Scholar]
  81. Gentemann, C.; Scott, J.P.; Mazzini, P.L.; Pianca, C.; Akella, S.; Minnett, P.J.; Cornillon, P.; Fox-Kemper, B.; Cetinić, I.; Chin, T.M. Saildrone: Adaptively sampling the marine environment. Bull. Am. Meteorol. Soc. 2020, 101, E744–E762. [Google Scholar] [CrossRef]
  82. Breivik, M.; Hovstein, V.E.; Fossen, T.I. Straight-line target tracking for unmanned surface vehicles. Model. Identif. Control. (MIC) 2008, 29, 131–149. [Google Scholar] [CrossRef]
  83. Son, N.-S.; Park, H.S.; Pyo, C.S. On the sea trial test of the autonomous collision avoidance among multiple unmanned surface vehicles. In Proceedings of the OCEANS 2023, Limerick, Ireland, 5–8 June 2023; pp. 1–6. [Google Scholar]
  84. Chiodi, A.M.; Zhang, C.; Cokelet, E.D.; Yang, Q.; Mordy, C.W.; Gentemann, C.L.; Cross, J.N.; Lawrence-Slavas, N.; Meinig, C.; Steele, M. Exploring the Pacific Arctic seasonal ice zone with saildrone USVs. Front. Mar. Sci. 2021, 8, 640690. [Google Scholar] [CrossRef]
  85. Manley, J.E. Development of the autonomous surface craft “aces”. In Proceedings of the Oceans’ 97. MTS/IEEE Conference Proceedings, Halifax, NS, Canada, 6–9 October 1997; pp. 827–832. [Google Scholar]
  86. Ebken, J.; Bruch, M.; Lum, J. Applying unmanned ground vehicle technologies to unmanned surface vehicles. In Proceedings of the Unmanned Ground Vehicle Technology VII, Orlando, FL, USA, 29–31 March 2005; pp. 585–596. [Google Scholar]
  87. Yang, W.-R.; Chen, C.-Y.; Hsu, C.-M.; Tseng, C.-J.; Yang, W.-C. Multifunctional inshore survey platform with unmanned surface vehicles. Int. J. Autom. Smart Technol. 2011, 1, 19–25. [Google Scholar] [CrossRef]
  88. Naeem, W.; Xu, T.; Sutton, R.; Tiano, A. The design of a navigation, guidance, and control system for an unmanned surface vehicle for environmental monitoring. Proc. Inst. Mech. Eng. Part M J. Eng. Marit. Environ. 2008, 222, 67–79. [Google Scholar] [CrossRef]
  89. Caccia, M.; Bibuli, M.; Bono, R.; Bruzzone, G.; Bruzzone, G.; Spirandelli, E. Unmanned surface vehicle for coastal and protected waters applications: The Charlie project. Mar. Technol. Soc. J. 2007, 41, 62–71. [Google Scholar] [CrossRef]
  90. Bibuli, M.; Caccia, M.; Lapierre, L.; Bruzzone, G. Guidance of unmanned surface vehicles: Experiments in vehicle following. IEEE Robot. Autom. Mag. 2012, 19, 92–102. [Google Scholar] [CrossRef]
  91. Pascoal, A.; Silvestre, C.; Oliveira, P. Vehicle and mission control of single and multiple autonomous marine robots. IEEE Control. Eng. Ser. 2006, 69, 353. [Google Scholar] [CrossRef]
  92. Martins, A.; Ferreira, H.; Almeida, C.; Silva, H.; Almeida, J.M.; Silva, E. Roaz and roaz ii autonomous surface vehicle design and implementation. In Proceedings of the International lifesaving congress, Matosinhos, Portugal, 27–29 September 2007. [Google Scholar]
  93. Majohr, J.; Buch, T. Modelling, Simulation and Control of an Autonomous Surface Marine Vehicle for Surveying Applications Measuring Dolphin MESSIN; IEE Control Engineering Series: London, UK, 2006; Volume 69, p. 329. [Google Scholar]
  94. Brekke, E.F.; Eide, E.; Eriksen, B.-O.H.; Wilthil, E.F.; Breivik, M.; Skjellaug, E.; Helgesen, Ø.K.; Lekkas, A.M.; Martinsen, A.B.; Thyri, E.H. milliAmpere: An autonomous ferry prototype. Proc. J. Phys. Conf. Ser. 2022, 2311, 012029. [Google Scholar] [CrossRef]
  95. Liu, T.; Liu, W. The USV of Spaceflight Xing Guang to the Olympic game with science and technology. Manag. Spacefl. Ind. 2008, 8, 46. [Google Scholar]
  96. Dunbabin, M.; Grinham, A. Experimental evaluation of an autonomous surface vehicle for water quality and greenhouse gas emission monitoring. In Proceedings of the 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, USA, 3–7 May 2010; pp. 5268–5274. [Google Scholar]
  97. Nicholson, D.P.; Michel, A.P.; Wankel, S.D.; Manganini, K.; Sugrue, R.A.; Sandwith, Z.O.; Monk, S.A. Rapid mapping of dissolved methane and carbon dioxide in coastal ecosystems using the ChemYak autonomous surface vehicle. Environ. Sci. Technol. 2018, 52, 13314–13324. [Google Scholar] [CrossRef] [PubMed]
  98. Leira, F.S.; Helgesen, H.H.; Johansen, T.A.; Fossen, T.I. Object detection, recognition, and tracking from UAVs using a thermal camera. J. Field Robot. 2021, 38, 242–267. [Google Scholar] [CrossRef]
  99. Peeters, G.; Catoor, T.; Afzal, M.R.; Kotze, M.; Geenen, P.; Van Baelen, S.; Vanierschot, M.; Boonen, R.; Slaets, P. Design and build of a scale model unmanned inland cargo vessel: Actuation and control architecture. In Proceedings of the Journal of Physics: Conference SeriesTrondheim, Trondheim, Norway, 13–14 November 2019; p. 012016. [Google Scholar]
  100. Brushane, F.; Jämsä, K.; Lafond, S.; Lilius, J. A Experimental Research Platform for Maritime Automation and Autonomous Surface Ship Applications. IFAC-PapersOnLine 2021, 54, 390–394. [Google Scholar] [CrossRef]
  101. Sauzé, C.; Neal, M.; Blanchard, T.; Miller, P. An Ice Strengthened Autonomous Surface Vessel for Surveying Marine-Terminating Calving Glaciers. J. Ocean. Technol. 2015, 10, 86–111. [Google Scholar]
  102. Bazilchuk, N. “Pamela” Makes Studying the Ocean Easy and Affordable. Available online: (accessed on 3 September 2023).
  103. Aker Arctic. Autonomous Ship. Available online: (accessed on 3 September 2023).
  104. Li, C.; Weeks, E.; Huang, W.; Milan, B.; Wu, R. Weather-induced transport through a tidal channel calibrated by an unmanned boat. J. Atmos. Ocean. Technol. 2018, 35, 261–279. [Google Scholar] [CrossRef]
  105. Bovcon, B.; Kristan, M. WaSR—A water segmentation and refinement maritime obstacle detection network. IEEE Trans. Cybern. 2021, 52, 12661–12674. [Google Scholar] [CrossRef] [PubMed]
  106. Cheng, Y.; Xu, H.; Liu, Y. Robust small object detection on the water surface through fusion of camera and millimeter wave radar. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, BC, Canada, 11–17 October 2021; pp. 15263–15272. [Google Scholar]
  107. Cheng, Y.; Zhu, J.; Jiang, M.; Fu, J.; Pang, C.; Wang, P.; Sankaran, K.; Onabola, O.; Liu, Y.; Liu, D. Flow: A dataset and benchmark for floating waste detection in inland waters. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, BC, Canada, 11–17 October 2021; pp. 10953–10962. [Google Scholar]
  108. Mahacek, P. Dynamic analysis of a SWATH vessel. MBARI Internsh. Rep. 2005, 1–13. [Google Scholar]
  109. Bingham, B.; Kraus, N.; Howe, B.; Freitag, L.; Ball, K.; Koski, P.; Gallimore, E. Passive and active acoustics using an autonomous wave glider. J. Field Robot. 2012, 29, 911–923. [Google Scholar] [CrossRef]
  110. Furfaro, T.C.; Dusek, J.E.; von Ellenrieder, K.D. Design, construction, and initial testing of an autonomous surface vehicle for riverine and coastal reconnaissance. In Proceedings of the OCEANS, Online, 26–29 October 2009; pp. 1–6. [Google Scholar]
  111. Zhao, T.; Han, C.; Xu, Y. Application of unmanned surface vehicle in urban river water quality monitoring. China Water Wastewater 2021, 37, 7. [Google Scholar] [CrossRef]
  112. Frizzell-Makowski, L.; Shelsby, R.; Mann, J.; Scheidt, D. An autonomous energy harvesting station-keeping vehicle for persistent ocean surveillance. In Proceedings of the OCEANS’11 MTS/IEEE KONA, Waikoloa, HI, USA, 19–22 September 2011; pp. 1–4. [Google Scholar]
  113. Pastore, T.; Djapic, V. Improving autonomy and control of autonomous surface vehicles in port protection and mine countermeasure scenarios. J. Field Robot. 2010, 27, 903–914. [Google Scholar] [CrossRef]
  114. Bremer, R.; Cleophas, P.; Fitski, H.; Keus, D. Unmanned Surface and Underwater Vehicles; DTIC Document; TNO Defence Security and Safety: Delft, The Netherlands, 2007. [Google Scholar]
  115. Murray, J. Sentry–An Unmanned Swimmer Intercept System; DTIC Document; QinetiQ North America Inc.: Waltham, MA, USA, 2008. [Google Scholar]
  116. Oleynikova, E.; Lee, N.B.; Barry, A.J.; Holler, J.; Barrett, D. Perimeter patrol on autonomous surface vehicles using marine radar. In Proceedings of the OCEANS’10 IEEE SYDNEY, Sydney, NSW, Australia, 24–27 May 2010; pp. 1–5. [Google Scholar]
  117. Vaneck, T.W.; RODRIGUEZ-ORTIZ, C.D.; Schmidt, M.C.; Manley, J.E. Automated bathymetry using an autonomous surface craft. Navigation 1996, 43, 407–419. [Google Scholar] [CrossRef]
  118. Roberts, G.N.; Sutton, R. Advances in Unmanned Marine Vehicles; IET: Roslyn Heights, NY, USA, 2006; Volume 69. [Google Scholar]
  119. Yakimenko, O.A.; Kragelund, S.P. Real-time optimal guidance and obstacle avoidance for UMVs. In Autonomous Underwater Vehicles; IntechOpen: London, UK, 2011; pp. 67–98. [Google Scholar] [CrossRef]
  120. Kulkarni, K.; Goerlandt, F.; Li, J.; Banda, O.V.; Kujala, P. Preventing shipping accidents: Past, present, and future of waterway risk management with Baltic Sea focus. Saf. Sci. 2020, 129, 104798. [Google Scholar] [CrossRef]
  121. Brown, H.C.; Jenkins, L.K.; Meadows, G.A.; Shuchman, R.A. BathyBoat: An autonomous surface vessel for stand-alone survey and underwater vehicle network supervision. Mar. Technol. Soc. J. 2010, 44, 20–29. [Google Scholar] [CrossRef]
  122. Carlson, D.F.; Fürsterling, A.; Vesterled, L.; Skovby, M.; Pedersen, S.S.; Melvad, C.; Rysgaard, S. An affordable and portable autonomous surface vehicle with obstacle avoidance for coastal ocean monitoring. HardwareX 2019, 5, e00059. [Google Scholar] [CrossRef]
  123. Carlson, D.F.; Akbulut, S.; Rasmussen, J.F.; Hestbech, C.S.; Andersen, M.H.; Melvad, C. Compact and modular autonomous surface vehicle for water research: The Naval Operating Research Drone Assessing Climate Change (NORDACC). HardwareX 2023, 15, e00453. [Google Scholar] [CrossRef] [PubMed]
  124. Meinig, C.; Lawrence-Slavas, N.; Jenkins, R.; Tabisola, H.M. The use of Saildrones to examine spring conditions in the Bering Sea: Vehicle specification and mission performance. In Proceedings of the OCEANS 2015-MTS/IEEE, Washington, DC, USA, 19–22 October 2015; pp. 1–6. [Google Scholar]
  125. Matos, A.; Silva, E.; Cruz, N.A.; Alves, J.C.; Almeida, D.; Pinto, M.; Martins, A.; Almeida, J.M.; Machado, D. Development of an Unmanned Capsule for large-scale maritime search and rescue. In Proceedings of the 2013 OCEANS, San Diego, CA, USA, 23–27 September 2013; pp. 1–8. [Google Scholar]
  126. Martins, A.; Dias, A.; Almeida, J.; Ferreira, H.; Almeida, C.; Amaral, G.; Machado, D.; Sousa, J.; Pereira, P.; Matos, A. Field experiments for marine casualty detection with autonomous surface vehicles. In Proceedings of the 2013 OCEANS, San Diego, CA, USA, 21–25 October 2013; pp. 1–5. [Google Scholar]
  127. Matias, B.; Oliveira, H.; Almeida, J.M.; Dias, A.; Ferreira, H.; Martins, A.; Silva, E. High-accuracy low-cost RTK-GPS for an unmannned surface vehicle. In Proceedings of the OCEANS 2015, Genova, Italy, 18–21 May 2015; pp. 1–4. [Google Scholar]
  128. Matos, A.; Martins, A.; Dias, A.; Ferreira, B.; Almeida, J.M.; Ferreira, H.; Amaral, G.; Figueiredo, A.; Almeida, R.; Silva, F. Multiple robot operations for maritime search and rescue in euRathlon 2015 competition. In Proceedings of the OCEANS 2016, Shanghai, China, 10–13 April 2016; pp. 1–7. [Google Scholar]
  129. Marques, M.M.; Martins, A.; Matos, A.; Cruz, N.A.; Almeida, J.M.; Alves, J.C.; Lobo, V.; Silva, E. REX 2014—Robotic Exercises 2014 multi-robot field trials. In Proceedings of the OCEANS 2015—MTS/IEEE, Washington, DC, USA, 19–22 October 2015; pp. 1–6. [Google Scholar]
  130. Murphy, R.R.; Steimle, E.; Hall, M.; Lindemuth, M.; Trejo, D.; Hurlebaus, S.; Medina-Cetina, Z.; Slocum, D. Robot-assisted bridge inspection. J. Intell. Robot. Syst. 2011, 64, 77–95. [Google Scholar] [CrossRef]
  131. Steimle, E.T.; Murphy, R.R.; Lindemuth, M.; Hall, M.L. Unmanned marine vehicle use at Hurricanes Wilma and Ike. In Proceedings of the OCEANS, Biloxi, MI, USA, 26–29 October 2009; pp. 1–6. [Google Scholar]
  132. Wang, J.; Gu, W.; Zhu, J.; Zhang, J. An unmanned surface vehicle for multi-mission applications. In Proceedings of the 2009 International Conference on Electronic Computer Technology, Macau, China, 20–22 February 2009; pp. 358–361. [Google Scholar]
  133. Wilde, G.A.; Murphy, R.R. User Interface for Unmanned Surface Vehicles Used to Rescue Drowning Victims. In Proceedings of the 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), Philadelphia, PA, USA, 6–8 August 2018; pp. 1–8. [Google Scholar]
  134. Bruzzone, G.; Odetti, A.; Caccia, M.; Ferretti, R. Monitoring of Sea-Ice-Atmosphere Interface in the Proximity of Arctic Tidewater Glaciers: The Contribution of Marine Robotics. Remote Sens. 2020, 12, 1707. [Google Scholar] [CrossRef]
  135. Giron-Sierra, J.M.; Sombria, J.C. Application of teams of usvs for cyanobacteria monitoring: Initial steps. IFAC-PapersOnLine 2021, 54, 416–421. [Google Scholar] [CrossRef]
  136. Jo, W.; Park, J.-H.; Hoashi, Y.; Min, B.-C. Development of an Unmanned Surface Vehicle for Harmful Algae Removal. In Proceedings of the OCEANS 2019 MTS/IEEE, Seattle, DC, USA, 27–31 October 2019; pp. 1–7. [Google Scholar]
  137. Powers, C.W.; Predosa, R.; Higgins, C.; Schmale III, D.G. Mobile distributed temperature sensing of the air–water interface of an aquatic environment with an unmanned surface vehicle. J. Unmanned Veh. Syst. 2017, 6, 43–56. [Google Scholar] [CrossRef]
  138. Idris, M.H.B.M.; Kamarudin, M.A.A.B.C.; Sahalan, M.I.; Abidin, Z.B.Z.; Rashid, M.M. Design and development of an autonomous surface vessel for inland water depth monitoring. In Proceedings of the 2016 International Conference on Computer and Communication Engineering (ICCCE), Kuala Lumpur, Malaysia, 26–27 July 2016; pp. 177–182. [Google Scholar]
  139. Makar, A.; Specht, C.; Specht, M.; Dąbrowski, P.; Szafran, M. Integrated Geodetic and Hydrographic Measurements of the Yacht Port for Nautical Charts and Dynamic Spatial Presentation. Geosciences 2020, 10, 203. [Google Scholar] [CrossRef]
  140. Vasilijević, A.; Nađ, Đ.; Mandić, F.; Mišković, N.; Vukić, Z. Coordinated navigation of surface and underwater marine robotic vehicles for ocean sampling and environmental monitoring. IEEE/ASME Trans. Mechatron. 2017, 22, 1174–1184. [Google Scholar] [CrossRef]
  141. Maawali, W.A.; Al Naabi, A.; Al Yaruubi, M.; Saleem, A.; Maashri, A.A. Design and Implementation of an Unmanned Surface Vehicle for Oil Spill Handling. In Proceedings of the 2019 1st International Conference on Unmanned Vehicle Systems-Oman (UVS), Muscat, Oman, 5–7 February 2019; pp. 1–6. [Google Scholar]
  142. Han, J.; Park, J.; Kim, T.; Kim, J. Precision navigation and mapping under bridges with an unmanned surface vehicle. Auton. Robot. 2015, 38, 349–362. [Google Scholar] [CrossRef]
  143. Wei, S.; Xiao, Y.; Yang, X.; Wang, H. Attitude Estimation Method for Target Ships Based on LiDAR Point Clouds via An Improved RANSAC. J. Mar. Sci. Eng. 2023, 11, 1755. [Google Scholar] [CrossRef]
  144. Du, Z.; Reppa, V.; Negenborn, R.R. MPC-based COLREGS Compliant Collision Avoidance for a Multi-Vessel Ship-Towing System. In Proceedings of the 2021 European Control Conference (ECC), Rotterdam, The Netherlands, 29 June–2 July 2021; pp. 1857–1862. [Google Scholar]
  145. Volden, Ø.; Stahl, A.; Fossen, T.I. Vision-based positioning system for auto-docking of unmanned surface vehicles (USVs). Int. J. Intell. Robot. Appl. 2022, 6, 86–103. [Google Scholar] [CrossRef]
  146. Zhang, M.; Liu, Z.; Cai, W.; Yan, Q. Design of Low-cost Unmanned Surface Vessel for Water Surface Cleaning. In Proceedings of the 2021 China Automation Congress (CAC), Beijing, China, 22–24 October 2021; pp. 2290–2293. [Google Scholar]
  147. Saarniniemi, T. Development of Hardware and Control in an Autonomous Ship Model; Aalto University: Helsinki, Finland, 2023. [Google Scholar]
  148. Neto, A.F.D.S.; Honório, L.D.M.; Da Silva, M.F.; Junior, I.C.D.S.; Westin, L.G.F. Development of Optimal Parameter Estimation Methodologies Applied to a 3DOF Autonomous Surface Vessel. IEEE Access 2021, 9, 50035–50049. [Google Scholar] [CrossRef]
  149. Fossen, S.; Fossen, T.I. Five-State Extended Kalman Filter for Estimation of Speed over Ground (SOG), Course over Ground (COG) and Course Rate of Unmanned Surface Vehicles (USVs): Experimental Results. Sensors 2021, 21, 7910. [Google Scholar] [CrossRef] [PubMed]
  150. Silva Junior, A.G.d.; Santos, D.H.d.; Negreiros, A.P.F.d.; Silva, J.M.V.B.d.S.; Gonçalves, L.M.G. High-Level Path Planning for an Autonomous Sailboat Robot Using Q-Learning. Sensors 2020, 20, 1550. [Google Scholar] [CrossRef]
  151. De Schaetzen, R.; Botros, A.; Gash, R.; Murrant, K.; Smith, S.L. Real-Time Navigation for Autonomous Surface Vehicles In Ice-Covered Waters. arXiv 2023, arXiv:2302.11601. [Google Scholar]
  152. Dos Santos, M.F.; Neto, A.F.D.S.; Honório, L.D.M.; Da Silva, M.F.; Mercorelli, P. Robust and Optimal Control Designed for Autonomous Surface Vessel Prototypes. IEEE Access 2023, 11, 9597–9612. [Google Scholar] [CrossRef]
  153. Utne, I.B. Risk-aware autonomous systems for safe and intelligent decision making. Plenary Sess. Talk 2023. [Google Scholar]
  154. Al Maawali, W.; Mesbah, M.; Al Maashri, A.; Saleem, A. Design of intelligent thruster decision-making system for USVs. Ocean. Eng. 2023, 285, 115431. [Google Scholar] [CrossRef]
  155. Bibuli, M.; Ferretti, R.; Odetti, A.; Cosso, T. River Survey Evolution by means of Autonomous Surface Vehicles. In Proceedings of the 2021 International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters (MetroSea), Reggio Calabria, Italy, 4–6 October 2021; pp. 412–417. [Google Scholar]
  156. Cruz, N.A.; Alves, J.C. Ocean sampling and surveillance using autonomous sailboats. In Proceedings of the 1st International Robotic Sailing Conference, Guildford, UK, 4–6 September 2008. [Google Scholar]
  157. Solnør, P.; Volden, Ø.; Gryte, K.; Petrovic, S.; Fossen, T.I. Hijacking of unmanned surface vehicles: A demonstration of attacks and countermeasures in the field. J. Field Robot. 2022, 39, 631–649. [Google Scholar] [CrossRef]
  158. Volden, Ø.; Solnør, P.; Petrovic, S.; Fossen, T.I. Secure and efficient transmission of vision-based feedback control signals. J. Intell. Robot. Syst. 2021, 103, 26. [Google Scholar] [CrossRef]
  159. ITTC. Procedure and Guidelines—Propulsive Performance Prediction; ITTC: Tokyo, Japan, 2011. [Google Scholar]
  160. Gadd, G. Some Effects of Scale in Ship Model Testing; Universität Hamburg, Institut für Schiffbau: Hamburg, Germany, 1982. [Google Scholar]
  161. Matala, R.; Suominen, M. Scaling principles for model testing in old brash ice channel. Cold Reg. Sci. Technol. 2023, 210, 103857. [Google Scholar] [CrossRef]
  162. Politis. Ship Resistance and Propulsion; National Technical University of Athens: Athens, Greece, 2011. [Google Scholar]
  163. Chun, D.-H.; Roh, M.-I.; Lee, H.-W.; Ha, J.; Yu, D. Deep reinforcement learning-based collision avoidance for an autonomous ship. Ocean. Eng. 2021, 234, 109216. [Google Scholar] [CrossRef]
  164. Hound, N. Yara Birkeland Autonomous and Zero Emission Vessel Test Model Design Demonstrated. Available online: (accessed on 31 October 2023).
  165. Alves, J.C.; Cruz, N.A. FASt—An autonomous sailing platform for oceanographic missions. In Proceedings of the OCEANS 2008, Cardiff, UK, 15–18 September 2008; pp. 1–7. [Google Scholar]
  166. Powers, C.W.; Hanlon, R.; Grothe, H.; Prussin, A.J.; Marr, L.C.; Schmale, D.G. Coordinated Sampling of Microorganisms Over Freshwater and Saltwater Environments Using an Unmanned Surface Vehicle (USV) and a Small Unmanned Aircraft System (sUAS). Front. Microbiol. 2018, 9, 1668. [Google Scholar] [CrossRef]
  167. Bittencourt, L.; Soares-Filho, W.; de Lima, I.M.S.; Pai, S.; Lailson-Brito, J., Jr.; Barreira, L.M.; Azevedo, A.F.; Guerra, L.A.A. Mapping cetacean sounds using a passive acoustic monitoring system towed by an autonomous Wave Glider in the Southwestern Atlantic Ocean. Deep. Sea Res. Part I Oceanogr. Res. Pap. 2018, 142, 58–68. [Google Scholar] [CrossRef]
  168. Bačkalov, I. Safety of autonomous inland vessels: An analysis of regulatory barriers in the present technical standards in Europe. Saf. Sci. 2020, 128, 104763. [Google Scholar] [CrossRef]
  169. Fossen, T.I.; Marine Control Systems–Guidance. Navigation, and Control of Ships, Rigs and Underwater Vehicles; Marine Cybernetics: Trondheim, Norway, 2002; Org. Number NO 985 195 005 MVA; ISBN 8292356002. Available online: (accessed on 6 December 2023).
  170. Hwang, T.; Youn, I.-H. Development of a Graph-Based Collision Risk Situation Model for Validation of Autonomous Ships’ Collision Avoidance Systems. J. Mar. Sci. Eng. 2023, 11, 2037. [Google Scholar] [CrossRef]
  171. Van Baelen, S.; Drijkoningen, S.; Moons, C.; Afzal, M.R.; Slaets, P. Experimental identification of the dynamic characteristics for a 1/25 scale model of the watertruck+ self-propelling barge. In Proceedings of the OCEANS 2019, Marseille, France, 17–20 June 2019; pp. 1–7. [Google Scholar]
  172. Du, Z.; Negenborn, R.R.; Reppa, V. Review of floating object manipulation by autonomous multi-vessel systems. Annu. Rev. Control. 2022, 55, 255–278. [Google Scholar] [CrossRef]
  173. Stefani, A. An Introduction to Ship Automation and Control Systems; Institute of Marine Engineering, Science & Technology: London, UK, 2013. [Google Scholar]
  174. Bolbot, V.; Owen, D.; Chaal, M.; BahooToroody, A.; Bergström, M.; Rahikainen, M.; Banda, O.V. Investigation of Statutory and Class society Based Requirements for Electronic Lookout. In Proceedings of the European Conference on Safety and Reliability, Southampton, UK, 3–8 September 2023. [Google Scholar]
  175. Feng, T.; Xiong, J.; Xiao, J.; Liu, J.; He, Y. Real-time riverbank line detection for USV system. In Proceedings of the 2019 IEEE International Conference on Mechatronics and Automation (ICMA), Tianjin, China, 4–7 August 2019; pp. 2546–2551. [Google Scholar]
  176. Xiong, J.; Li, D.; He, Y.; Gu, F.; Han, J. Active quasi-LPV modeling and identification for a Water-Jet Propulsion USV: An experimental study. IFAC-PapersOnLine 2015, 48, 1359–1364. [Google Scholar] [CrossRef]
  177. Xiong, J.; He, Y.; Gu, F.; Li, D.; Han, J. Quasi-lpv modeling and identification for a water-jet propulsion usv: An experimental study. In Proceedings of the 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014), Bali, Indonesia, 5–10 December 2014; pp. 431–436. [Google Scholar]
  178. Bolbot, V.T.G.; Wennersberg, L.A. A method to identify and rank objects and hazardous interactions affecting autonomous ships navigation. J. Navig. 2022, 75, 572–593. [Google Scholar] [CrossRef]
  179. Guo, C.; Haugen, S.; Utne, I.B. Risk assessment of collisions of an autonomous passenger ferry. Proc. Inst. Mech. Eng. Part O J. Risk Reliab. 2023, 237, 425–435. [Google Scholar] [CrossRef]
  180. Maidana, R.G.; Kristensen, S.D.; Utne, I.B.; Sørensen, A.J. Risk-based path planning for preventing collisions and groundings of maritime autonomous surface ships. Ocean. Eng. 2023, 290, 116417. [Google Scholar] [CrossRef]
  181. Bolbot, V.; Theotokatos, G.; Hamann, R.; Psarros, G.; Boulougouris, E. Dynamic Blackout Probability Monitoring System for Cruise Ship Power Plants. Energies 2021, 14, 6598. [Google Scholar] [CrossRef]
  182. Basnet, S.; BahooToroody, A.; Bolbot, V.; Valdez Banda, O.A. Real-Time Risk Monitoring of Ship Pilotage Operations: Automating BN Risk Model Development. In Proceedings of the The 33rd European Safety and Reliability Conference (ESREL 2023), Southampton, UK, 3–8 September 2023. [Google Scholar]
  183. Fan, C.; Montewka, J.; Zhang, D. Towards a Framework of Operational-Risk Assessment for a Maritime Autonomous Surface Ship. Energies 2021, 14, 3879. [Google Scholar] [CrossRef]
  184. Rødseth, Ø.J.; Faivre, J.; Hjørungnes, S.R.; Andersen, P.; Bolbot, V.; Pauwelyn, A.-S.; Wennersberg, L.A. AUTOSHIP Deliverable D3.1 Autonomous Ship Design Standards. Revision 2.0. 2020. Available online: (accessed on 6 December 2023).
  185. Basnet, S.; BahooToroody, A.; Chaal, M.; Lahtinen, J.; Bolbot, V.; Banda, O.A.V. Risk analysis methodology using STPA-based Bayesian network-applied to remote pilotage operation. Ocean. Eng. 2023, 270, 113569. [Google Scholar] [CrossRef]
  186. IMO. Revised Guidelines for the Onboard Operational Use of Shipborne Automatic Identification Systems (AIS) Resolution A.1106; IMO: London, UK, 2015. [Google Scholar]
  187. Juhász, K.; Póser, V.; Kozlovszky, M.; Bánáti, A. WiFi vulnerability caused by SSID forgery in the IEEE 802.11 protocol. In Proceedings of the 2019 IEEE 17th World Symposium on Applied Machine Intelligence and Informatics (SAMI), Piscataway, NJ, USA, 24–26 January 2019; pp. 333–338. [Google Scholar]
  188. Amro, A.; Gkioulos, V.; Katsikas, S. Assessing cyber risk in cyber-physical systems using the ATT&CK framework. ACM Trans. Priv. Secur. 2023, 26, 1–33. [Google Scholar] [CrossRef]
  189. Ullah, M.A.; Yastrebova, A.; Mikhaylov, K.; Höyhtyä, M.; Alves, H. Situational awareness for autonomous ships in the arctic: mMTC direct-to-satellite connectivity. IEEE Commun. Mag. 2022, 60, 32–38. [Google Scholar] [CrossRef]
  190. Höyhtyä, M.; Huusko, J.; Kiviranta, M.; Solberg, K.; Rokka, J. Connectivity for autonomous ships: Architecture, use cases, and research challenges. In Proceedings of the 2017 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Korea, 18–20 October 2017; pp. 345–350. [Google Scholar]
  191. Zhang, Y.; Hou, J.; Yuan, Y. A Comprehensive Study of the Robustness for LiDAR-Based 3D Object Detectors Against Adversarial Attacks. Int. J. Comput. Vis. 2023, 1–33. [Google Scholar] [CrossRef]
  192. Yoo, J.; Goerlandt, F.; Chircop, A. Unmanned remotely operated search and rescue ships in the Canadian Arctic: Exploring the opportunities, risk dimensions and governance implications. In Governance of Arctic Shipping: Rethinking Risk, Human Impacts and Regulation; Springer: Berlin/Heidelberg, Germany, 2020; pp. 83–103. [Google Scholar]
  193. Morel, T.A.; Bejarano, G.; Manzano, J.M.; Orihuela, L. Experimental validation of robust non-linear state observers for autonomous surface vehicles equipped with position sensors. In Proceedings of the 2022 IEEE Conference on Control Technology and Applications (CCTA), Trieste, Italy, 23–25 August 2022; pp. 357–362. [Google Scholar]
  194. Dos Santos, D.S.; Nascimento, C.L.; Cunha, W.C. Autonomous navigation of a small boat using IMU/GPS/digital compass integration. In Proceedings of the 2013 IEEE International Systems Conference (SysCon), Orlando, FL, USA, 15–18 April 2013; pp. 468–474. [Google Scholar]
  195. Mattos, D.I.; Santos, D.S.d.; Nascimento, C.L. Development of a low-cost autonomous surface vehicle using MOOS-IvP. In Proceedings of the 2016 Annual IEEE Systems Conference (SysCon), Orlando, FL, USA, 18–21 April 2016; pp. 1–6. [Google Scholar]
  196. Da Silva, M.F.; Honório, L.D.M.; Dos Santos, M.F.; Neto, A.F.D.S.; Cruz, N.A.; Matos, A.; Westin, L.G.F. Project and Control Allocation of a 3 DoF Autonomous Surface Vessel With Aerial Azimuth Propulsion System. IEEE Access 2021, 9, 5212–5227. [Google Scholar] [CrossRef]
  197. Regina, B.A.; Honório, L.D.M.; Pancoti, A.A.N.; Silva, M.F.; Santos, M.F.; Lopes, V.M.L.; Neto, A.F.D.S.; Westin, L.G.F. Hull and Aerial Holonomic Propulsion System Design for Optimal Underwater Sensor Positioning in Autonomous Surface Vessels. Sensors 2021, 21, 571. [Google Scholar] [CrossRef] [PubMed]
  198. Souza, M.B.A.; Neto, A.F.D.S.; Honório, L.D.M.; de Oliveira, E.J.; Silva, M.F.; Pancoti, A. A Convolutional System Identification Approach Mixing Optimal Parameter Estimation and Deep Learning. Int. J. Control. Autom. Syst. 2023, 21, 2674–2684. [Google Scholar] [CrossRef]
  199. Santos, D.; Silva Junior, A.G.; Negreiros, A.; Vilas Boas, J.; Alvarez, J.; Araujo, A.; Aroca, R.V.; Gonçalves, L.M.G. Design and Implementation of a Control System for a Sailboat Robot. Robotics 2016, 5, 5. [Google Scholar] [CrossRef]
  200. Silva Junior, A.G.D.; Lima Sa, S.T.D.; Santos, D.H.D.; Negreiros, Á.P.F.D.; Souza Silva, J.M.V.B.D.; Álvarez Jácobo, J.E.; Garcia Gonçalves, L.M. Towards a Real-Time Embedded System for Water Monitoring Installed in a Robotic Sailboat. Sensors 2016, 16, 1226. [Google Scholar] [CrossRef]
  201. Boas, J.V.; Júnior, A.S.; Santos, D.; Negreiros, A.P.; Alvarez-Jácobo, J.; Gonçalves, L.M. Towards the electromechanical design of an autonomous robotic sailboat. In Proceedings of the 2016 XIII Latin American Robotics Symposium and IV Brazilian Robotics Symposium (LARS/SBR), Recife, Brazil, 8–12 October 2016; pp. 43–48. [Google Scholar]
  202. Santos, D.; Negreiros, A.; Jacobo, J.; Goncalves, L.; Junior, A.S.; Silva, J.M. Gain-scheduling pid low-level control for robotic sailboats. In Proceedings of the 2018 Latin American Robotic Symposium, 2018 Brazilian Symposium on Robotics (SBR) and 2018 Workshop on Robotics in Education (WRE), João Pessoa, Brazil, 6–10 November 2018; pp. 147–152. [Google Scholar]
  203. Codd-Downey, R.; Jenkin, M.; Dey, B.B.; Zacher, J.; Blainey, E.; Andrews, P. Monitoring re-growth of invasive plants using an autonomous surface vessel. Front. Robot. AI 2021, 7, 583416. [Google Scholar] [CrossRef]
  204. Wang, H.; Yin, Y.; Jing, Q. Comparative Analysis of 3D LiDAR Scan-Matching Methods for State Estimation of Autonomous Surface Vessel. J. Mar. Sci. Eng. 2023, 11, 840. [Google Scholar] [CrossRef]
  205. Hu, B.; Liu, X.; Jing, Q.; Lyu, H.; Yin, Y. Estimation of berthing state of maritime autonomous surface ships based on 3D LiDAR. Ocean. Eng. 2022, 251, 111131. [Google Scholar] [CrossRef]
  206. You, X.; Ma, F.; Lu, S.; Liu, J.; Yan, X. An integrated platform for the development of autonomous and remote-control ships. In Proceedings of the 19th Conference on Computer and IT Applications in the Maritime Industries (COMPIT 2020), Pontignano, Italy, 17–19 August 2020; pp. 316–327. [Google Scholar]
  207. Oceanalpha. Small USV. Available online: (accessed on 19 September 2023).
  208. You, X.; Li, S.; Liu, J.; Yan, X. Experimental research of the PID tune method for ship path following control. In Proceedings of the 33rd International Ocean and Polar Engineering Conference, Ottawa, ON, Canada, 19–23 June 2023. [Google Scholar]
  209. Du, B.; Lin, B.; Xie, W.; Zhang, W.; Negenborn, R.R.; Pang, Y. Flexible Collision-free Platooning Method for Unmanned Surface Vehicle with Experimental Validations. In Proceedings of the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 23–27 October 2022; pp. 6854–6860. [Google Scholar]
  210. Cao, H.; Guo, Z.; Wang, S.; Cheng, H.; Zhan, C. Intelligent Wide-Area Water Quality Monitoring and Analysis System Exploiting Unmanned Surface Vehicles and Ensemble Learning. Water 2020, 12, 681. [Google Scholar] [CrossRef]
  211. Liang, J.; Zhang, J.; Ma, Y.; Zhang, C.-Y. Derivation of Bathymetry from High-resolution Optical Satellite Imagery and USV Sounding Data. Mar. Geod. 2017, 40, 466–479. [Google Scholar] [CrossRef]
  212. Jin, J.; Zhang, J.; Liu, D. Design and Verification of Heading and Velocity Coupled Nonlinear Controller for Unmanned Surface Vehicle. Sensors 2018, 18, 3427. [Google Scholar] [CrossRef] [PubMed]
  213. Jin, J.; Zhang, J.; Shao, F. Modelling, manoeuvring analysis and course following for two unmanned surface vehicles driven by a single propeller and double propellers. In Proceedings of the The 27th Chinese Control and Decision Conference (2015 CCDC), Kyoto, Japan, 23–25 May 2015; pp. 4932–4937. [Google Scholar]
  214. Jin, J.; Zhang, J.; Shao, F.; Lv, Z.; Li, M.; Liu, L.; Zhang, P. Active and passive underwater acoustic applications using an unmanned surface vehicle. In Proceedings of the OCEANS 2016, Shanghai, China, 10–13 April 2016; pp. 1–6. [Google Scholar]
  215. Luo, Y.; Ai, J.; Zheng, J.; Wang, J. Control system design and thrust analysis of an unmanned surface cleaning vessel with a novel pump-valve propulsion system. IEEE Access 2020, 8, 46356–46372. [Google Scholar] [CrossRef]
  216. Paez, J.; Villa, J.; Cabrera-Gámez, J.; Yime, E. Implementation of an unmanned surface vehicle for environmental monitoring applications. In Proceedings of the 2018 IEEE 2nd Colombian Conference on Robotics and Automation (CCRA), Barranquilla, Colombia, 1–3 November 2018; pp. 1–6. [Google Scholar]
  217. Villa, J.; Paez, J.; Quintero, C.; Yime, E.; Cabrera-Gámez, J. Design and control of an unmanned surface vehicle for environmental monitoring applications. In Proceedings of the 2016 IEEE Colombian Conference on Robotics and Automation (CCRA), Ningbo, China, 29–30 September 2016; pp. 1–5. [Google Scholar]
  218. Vasilijević, A.; Buxton, B.; Sharvit, J.; Stilinovic, N.; Nad, D.; Miskovic, N.; Planer, D.; Hale, J.; Vukic, Z. An ASV for coastal underwater archaeology: The Pladypos survey of Caesarea Maritima, Israel. In Proceedings of the OCEANS 2015-Genova, Genova, Italy, 18–21 May 2015; pp. 1–7. [Google Scholar]
  219. Kapetanović, N.; Vasilijević, A.; Nađ, Đ.; Zubčić, K.; Mišković, N. Marine robots mapping the present and the past: Unraveling the secrets of the deep. Remote Sens. 2020, 12, 3902. [Google Scholar] [CrossRef]
  220. Kapetanović, N.; Kordić, B.; Vasilijević, A.; Nađ, Đ.; Mišković, N. Autonomous Vehicles Mapping Plitvice Lakes National Park, Croatia. Remote Sens. 2020, 12, 3683. [Google Scholar] [CrossRef]
  221. Desa, E.; Maurya, P.K.; Pereira, A.; Pascoal, A.M.; Prabhudesai, R.; Mascarenhas, A.; Desa, E.; Madhan, R.; Matondkar, S.; Navelkar, G. A small autonomous surface vehicle for ocean color remote sensing. IEEE J. Ocean. Eng. 2007, 32, 353–364. [Google Scholar] [CrossRef]
  222. Ferretti, R.; Bibuli, M.; Bruzzone, G.; Odetti, A.; Aracri, S.; Motta, C.; Caccia, M.; Rovere, M.; Mercorella, A.; Madricardo, F.; et al. Acoustic seafloor mapping using non-standard ASV: Technical challenges and innovative solutions. In Proceedings of the OCEANS 2023, Limerick, Ireland, 5–8 June 2023; pp. 1–6. [Google Scholar]
  223. Boscaino, V.; Odetti, A.; Marsala, G.; Di Cara, D.; Panzavecchia, N.; Caccia, M.; Tinè, G. A fuel cell powered autonomous surface vehicle: The Eco-SWAMP project. Int. J. Hydrog. Energy 2021, 46, 20732–20749. [Google Scholar] [CrossRef]
  224. Odetti, A.; Bruzzone, G.; Altosole, M.; Viviani, M.; Caccia, M. SWAMP, an Autonomous Surface Vehicle expressly designed for extremely shallow waters. Ocean Eng. 2020, 216, 108205. [Google Scholar] [CrossRef]
  225. Giordano, F.; Mattei, G.; Parente, C.; Peluso, F.; Santamaria, R. MicroVeGA (micro vessel for geodetics application): A marine drone for the acquisition of bathymetric data for GIS applications. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2015, 40, 123–130. [Google Scholar] [CrossRef]
  226. Giordano, F.; Mattei, G.; Parente, C.; Peluso, F.; Santamaria, R. Integrating Sensors into a Marine Drone for Bathymetric 3D Surveys in Shallow Waters. Sensors 2016, 16, 41. [Google Scholar] [CrossRef]
  227. Mattei, G.; Troisi, S.; Aucelli, P.P.C.; Pappone, G.; Peluso, F.; Stefanile, M. Sensing the Submerged Landscape of Nisida Roman Harbour in the Gulf of Naples from Integrated Measurements on a USV. Water 2018, 10, 1686. [Google Scholar] [CrossRef]
  228. Pappone, G.; Aucelli, P.P.C.; Mattei, G.; Peluso, F.; Stefanile, M.; Carola, A. A Detailed Reconstruction of the Roman Landscape and the Submerged Archaeological Structure at “Castel dell’Ovo islet” (Naples, Southern Italy). Geosciences 2019, 9, 170. [Google Scholar] [CrossRef]
  229. Zappalà, G.; Bruzzone, G.; Caruso, G.; Azzaro, M. Development of an automatic sampler for extreme polar environments: First in situ application in Svalbard Islands. Rend. Lincei 2016, 27, 251–259. [Google Scholar] [CrossRef]
  230. Madeo, D.; Pozzebon, A.; Mocenni, C.; Bertoni, D. A low-cost unmanned surface vehicle for pervasive water quality monitoring. IEEE Trans. Instrum. Meas. 2020, 69, 1433–1444. [Google Scholar] [CrossRef]
  231. Garuglieri, S.; Madeo, D.; Pozzebon, A.; Zingone, R.; Mocenni, C.; Bertoni, D. An integrated system for real-time water monitoring based on low cost unmanned surface vehicles. In Proceedings of the 2019 IEEE Sensors Applications Symposium (SAS), Sophia Antipolis, France, 11–13 March 2019; pp. 1–6. [Google Scholar]
  232. Harada, K.; Watanabe, K.; Utsunomiya, K.; Shimpo, M.; Dzeng, R.-J. Experimental study on collision avoidance procedures for plastic waste cleaner USV. In Proceedings of the OCEANS 2019 MTS/IEEE SEATTLE, Seattle, WA, USA, 27–31 October 2019; pp. 1–6. [Google Scholar]
  233. Jung, J.; Park, J.; Choi, J.; Choi, H.-T. Navigation of unmanned surface vehicles using underwater geophysical sensing. IEEE Access 2020, 8, 208707–208717. [Google Scholar] [CrossRef]
  234. Han, J.; Park, J.; Kim, J. Three-dimensional reconstruction of bridge structures above the waterline with an unmanned surface vehicle. In Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, USA, 14–18 September 2014; pp. 2273–2278. [Google Scholar]
  235. Han, J.; Kim, J. Three-Dimensional Reconstruction of a Marine Floating Structure With an Unmanned Surface Vessel. IEEE J. Ocean. Eng. 2019, 44, 984–996. [Google Scholar] [CrossRef]
  236. Kim, D.; Shin, J.-U.; Kim, H.; Lee, D.; Lee, S.-M.; Myung, H. Development of jellyfish removal robot system JEROS. In Proceedings of the 2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Daejeon, Republic of Korea, 26–28 November 2012; pp. 599–600. [Google Scholar]
  237. Kim, H.; Kim, D.; Kim, H.; Shin, J.-U.; Myung, H. An extended any-angle path planning algorithm for maintaining formation of multi-agent jellyfish elimination robot system. Int. J. Control. Autom. Syst. 2016, 14, 598–607. [Google Scholar] [CrossRef]
  238. Kim, D.; Shin, J.-U.; Kim, H.; Kim, H.; Myung, H. Formation Control Experiment of Autonomous Jellyfish Removal Robot System JEROS. In Proceedings of the Robot Intelligence Technology and Applications 2: Results from the 2nd International Conference on Robot Intelligence Technology and Applications, Beijing, China, 6–8 November 2014; pp. 463–471. [Google Scholar]
  239. Kim, D.; Shin, J.-u.; Kim, H.; Lee, D.; Lee, S.-M.; Myung, H. Experimental tests of autonomous jellyfish removal robot system JEROS. In Robot Intelligence Technology and Applications 2012, Proceedings of the An Edition of the Presented Papers from the 1st International Conference on Robot Intelligence Technology and Applications, Gwangju, Republic of Korea, 16–18 2012; MIT: Cambridge, MA, USA, 2013; pp. 395–403. [Google Scholar]
  240. Kim, D.; Kim, H.; Kim, H.; Shin, J.-U.; Myung, H.; Kim, Y.-G. Path planning for multi-agent jellyfish removal robot system jeros and experimental tests. In Distributed Autonomous Robotic Systems, Proceedings of the 12th International Symposium, Berlin, Germany, 17–19 August 2016; Springer: Berlin/Heidelberg, Germany, 2016; pp. 299–310. [Google Scholar]
  241. Kim, K.; Hyun, J.; Choi, D.; Myung, H. Vertical thrusting unmanned surface vehicle for stable and close inspection of bridge structure. In Proceedings of the 2016 16th International Conference on Control, Automation and Systems (ICCAS), Gyeongju, Republic of Korea, 16–19 October 2016; pp. 1040–1042. [Google Scholar]
  242. Kim, J.-H.; Kim, S.-R.; Jo, H.-J.; Yeo, C.Y.; Yeo, D.J.; Yun, K.; Park, J.; Park, J.-Y. Development of automatic gain-tuning algorithm for heading control using free-running test data. Int. J. Nav. Archit. Ocean. Eng. 2023, 15, 100517. [Google Scholar] [CrossRef]
  243. Research Lab Autonomous Shipping. RAS Vessels, Sites and Experiments. Available online: (accessed on 3 September 2023).
  244. Haseltalab, A.; Negenborn, R.R. Model predictive maneuvering control and energy management for all-electric autonomous ships. Appl. Energy 2019, 251, 113308. [Google Scholar] [CrossRef]
  245. Piaggio, B.; Garofano, V.; Donnarumma, S.; Alessandri, A.; Negenborn, R.R.; Martelli, M. Follow-the-Leader Guidance, Navigation, and Control of Surface Vessels: Design and Experiments. IEEE J. Ocean. Eng. 2023, 48, 997–1008. [Google Scholar] [CrossRef]
  246. Pampus, M.J.v.; Haseltalab, A.; Garofano, V.; Reppa, V.; Deinema, Y.H.; Negenborn, R.R. Distributed Leader-Follower Formation Control for Autonomous Vessels based on Model Predictive Control. In Proceedings of the 2021 European Control Conference (ECC), Rotterdam, The Netherlands, 29 June–2 July 2021; pp. 2380–2387. [Google Scholar]
  247. Wang, W.; Mateos, L.A.; Park, S.; Leoni, P.; Gheneti, B.; Duarte, F.; Ratti, C.; Rus, D. Design, Modeling, and Nonlinear Model Predictive Tracking Control of a Novel Autonomous Surface Vehicle. In Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 21–25 May 2018; pp. 6189–6196. [Google Scholar]
  248. Wang, W.; Mateos, L.; Wang, Z.; Huang, K.W.; Schwager, M.; Ratti, C.; Rus, D. Cooperative Control of an Autonomous Floating Modular Structure Without Communication: Extended Abstract. In Proceedings of the 2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS), New Brunswick, NJ, USA, 22–23 August 2019; pp. 44–46. [Google Scholar]
  249. Mogstad, A.A.; Johnsen, G.; Ludvigsen, M. Shallow-Water Habitat Mapping using Underwater Hyperspectral Imaging from an Unmanned Surface Vehicle: A Pilot Study. Remote Sens. 2019, 11, 685. [Google Scholar] [CrossRef]
  250. Udjus, G. Force Field Identification and Positioning Control of an Autonomous Vessel Using Inertial Measurement Units; NTNU: Trondheim, Norway, 2017. [Google Scholar]
  251. Dirdal, J.A.; Skjetne, R.; Roháč, J.; Fossen, T.I. Online wave direction and wave number estimation from surface vessel motions using distributed inertial measurement arrays and phase-time-path-differences. Ocean. Eng. 2022, 249, 110760. [Google Scholar] [CrossRef]
  252. Ghani, M.H.; Hole, L.R.; Fer, I.; Kourafalou, V.H.; Wienders, N.; Kang, H.; Drushka, K.; Peddie, D. The SailBuoy remotely-controlled unmanned vessel: Measurements of near surface temperature, salinity and oxygen concentration in the Northern Gulf of Mexico. Methods Oceanogr. 2014, 10, 104–121. [Google Scholar] [CrossRef]
  253. Osen, O.L.; Havnegjerde, A.; Kamsvåg, V.; Liavaag, S.; Bye, R.T. A low cost USV for aqua farm inspection. In Proceedings of the 2016 Techno-Ocean (Techno-Ocean), Kobe, Japan, 6–8 October 2016; pp. 291–298. [Google Scholar]
  254. Skjetne, R.; Smogeli, Ø.; Fossen, T.I. Modeling, identification, and adaptive maneuvering of CyberShip II: A complete design with experiments. IFAC Proc. Vol. 2004, 37, 203–208. [Google Scholar] [CrossRef]
  255. Skjetne, R. The Maneuvering Problem. Ph.D.Thesis, NTNU, Trondheim, Norway, 2005. [Google Scholar]
  256. Balbuena, J.; Quiroz, D.; Song, R.; Bucknall, R.; Cuellar, F. Design and implementation of an USV for large bodies of fresh waters at the highlands of Peru. In Proceedings of the OCEANS 2017, Anchorage, AL, USA, 18–21 September 2017; pp. 1–8. [Google Scholar]
  257. Song, R.; Liu, Y.; Balbuena, J.; Cuellar, F.; Bucknall, R. Developing an energy effective autonomous usv for undertaking missions at the highlands of peru. In Proceedings of the 2018 OCEANS-MTS/IEEE Kobe Techno-Oceans (OTO), Kobe, Japan, 28–31 May 2018; pp. 1–7. [Google Scholar]
  258. Specht, M.; Specht, C.; Wąż, M.; Naus, K.; Grządziel, A.; Iwen, D. Methodology for Performing Territorial Sea Baseline Measurements in Selected Waterbodies of Poland. Appl. Sci. 2019, 9, 3053. [Google Scholar] [CrossRef]
  259. Costa, A.C.; Xu, H.; Soares, C.G. Robust parameter estimation of an empirical manoeuvring model using free-running model tests. J. Mar. Sci. Eng. 2021, 9, 1302. [Google Scholar] [CrossRef]
  260. Hinostroza, M.; Xu, H.; Soares, C.G. Experimental results of the cooperative operation of autonomous surface vehicles navigating in complex marine environment. Ocean. Eng. 2021, 219, 108256. [Google Scholar] [CrossRef]
  261. Xu, H.; Hinostroza, M.A.; Wang, Z.; Soares, C.G. Experimental investigation of shallow water effect on vessel steering model using system identification method. Ocean. Eng. 2020, 199, 106940. [Google Scholar] [CrossRef]
  262. Cruz, N.A.; Matos, A.; Cunha, S.; da Silva, S.O. Zarco-An Autonomous Craft for Underwater Surveys. In Proceedings of the 7th Geomatic Week 2007, Barcelona, Spain, 20–23 February 2007. [Google Scholar]
  263. Matos, A.; Cruz, N.A. Positioning control of an underactuated surface vessel. In Proceedings of the OCEANS 2008, Quebec City, QC, Canada, 15–18 September 2008; pp. 1–5. [Google Scholar]
  264. Cruz, N.A.; Alves, J.C.; Guedes, T.; Rodrigues, R.; Pinto, V.; Campos, D.; Silva, D. Integration of wind propulsion in an electric ASV. In Proceedings of the World Robotic Sailing championship and International Robotic Sailing Conference, Mariehamn, Åland (Finland), 31 August–4 September 2015; pp. 15–27. [Google Scholar]
  265. Silva, S.R.; Cunha, S.; Matos, A.; Cruz, N.A. Shallow water height mapping with interferometric synthetic aperture sonar. In Proceedings of the OCEANS 2008, Quebec City, QC, Canada, 15–18 September 2008; pp. 1–7. [Google Scholar]
  266. Silva, S.R.; Cunha, S.; Matos, A.; Cruz, N.A. An Autonomous Boat Based Synthetic Aperture Sonar. In Proceedings of the OCEANS 2007, Vancouver, BC, Canada, 29 September–4 October 2007; pp. 1–7. [Google Scholar]
  267. Pinto, M.; Ferreira, B.; Matos, A.; Cruz, N.A. Side scan sonar image segmentation and feature extraction. In Proceedings of the OCEANS 2009, Biloxi, MI, USA, 26–29 October 2009; pp. 1–9. [Google Scholar]
  268. Silva, S.R.; Cunha, S.; Matos, A.; Cruz, N.A. Shallow water surveying using experimental interferometric synthetic aperture sonar. Mar. Technol. Soc. J. 2009, 43, 50–63. [Google Scholar] [CrossRef]
  269. Alves, J.C.; Ramos, T.; Cruz, N.A. A recongurable computing system for an autonomous sailboat. In Proceedings of the 1st International Robotic Sailing Conference, Guildford, UK, 4–6 September 2008. [Google Scholar]
  270. Alves, J.C.; Cruz, N.A. A mission programming system for an autonomous sailboat. In Proceedings of the 2014 Oceans—St. John’s, St. John’s, NL, Canada, 14–19 September 2014; pp. 1–7. [Google Scholar]
  271. Xiao, L.; Alves, J.C.; Cruz, N.A.; Jouffroy, J. Online speed optimization for sailing yachts using extremum seeking. In Proceedings of the 2012 Oceans, Hampton Roads, VA, USA, 14–19 October 2012; pp. 1–6. [Google Scholar]
  272. Cruz, N.A.; Alves, J.C. Auto-heading controller for an autonomous sailboat. In Proceedings of the OCEANS’10 IEEE SYDNEY, Sydney, NSW, Australia, 24–27 May 2010; pp. 1–6. [Google Scholar]
  273. Sousa, D.; Luís, M.; Sargento, S.; Pereira, A. An Aquatic Mobile Sensing USV Swarm with a Link Quality-Based Delay Tolerant Network. Sensors 2018, 18, 3440. [Google Scholar] [CrossRef]
  274. Sousa, D.; Hernandez, D.; Oliveira, F.; Luís, M.; Sargento, S. A Platform of Unmanned Surface Vehicle Swarms for Real Time Monitoring in Aquaculture Environments. Sensors 2019, 19, 4695. [Google Scholar] [CrossRef]
  275. Sousa, D.; Sargento, S.; Pereira, A.; Luís, M. Self-adaptive team of aquatic drones with a communication network for aquaculture. In Proceedings of the Progress in Artificial Intelligence: 19th EPIA Conference on Artificial Intelligence, EPIA 2019, Vila Real, Portugal, 3–6 September 2019; Proceedings, Part II 19. pp. 569–580. [Google Scholar]
  276. Patricio, J.; Luís, M.; Sargento, S. Passive Gateway Election Mechanisms for Swarms of Drones in Aquatic Sensing Environments. In Proceedings of the 2020 27th International Conference on Telecommunications (ICT), London, UK, 5–7 October 2020; pp. 1–6. [Google Scholar]
  277. Weston Robot. Unmanned Surface Vessel ELFIN. Available online: (accessed on 3 September 2023).
  278. Cabrera-Gámez, J.; Ramos de Miguel, A.; Domínguez-Brito, A.C.; Hernández-Sosa, J.D.; Isern-González, J.; Fernández-Perdomo, E. An embedded low-power control system for autonomous sailboats. In Proceedings of the Robotic Sailing 2013: 6th International Robotic Sailing Conference, Brest, France, 2–6 September 2013; pp. 67–79. [Google Scholar]
  279. Santana-Jorge, F.J.; Domínguez-Brito, A.C.; Cabrera-Gámez, J. A Component-Based C++ Communication Middleware for an Autonomous Robotic Sailboat. In Robotic Sailing 2017; Springer: Cham, Switzerland, 2018; pp. 39–54. [Google Scholar]
  280. Domínguez-Brito, A.C.; Valle-Fernández, B.; Cabrera-Gámez, J.; Ramos-de-Miguel, A.; García, J.C. A-TIRMA G2: An Oceanic Autonomous Sailboat. In Robotic Sailing 2015; Springer: Cham, Switzerland, 2016; pp. 3–13. [Google Scholar]
  281. Cabrera-Gámez, J.; Domínguez-Brito, A.C.; Santana-Jorge, F.; Gamo, D.; Jiménez, D.; Guerra, A.; Castro, J.J. Acoustic detection of tagged angelsharks from an autonomous sailboat. In Proceedings of the Robot 2019: Fourth Iberian Robotics Conference: Advances in Robotics, Porto, Portugal, 20–22 November 2020; Volume 1, pp. 295–304. [Google Scholar]
  282. Malheiro, B.; Silva, M.S.; Guedes, P.; Ferreira, P. Airfoil Selection and Wingsail Design for an Autonomous Sailboat. In Proceedings of the Robot 2019: Fourth Iberian Robotics Conference, Porto, Portugal, 20–22 November 2019. [Google Scholar]
  283. Giron-Sierra, J.M.; Gheorghita, A.T.; Angulo, G.; Jimenez, J.F. Preparing the automatic spill recovery by two unmanned boats towing a boom: Development with scale experiments. Ocean. Eng. 2015, 95, 23–33. [Google Scholar] [CrossRef]
  284. Giron-Sierra, J.M.; Gheorghita, A.T.; Jiménez, J. Fully automatic boom towing by unmanned ships: Experimental study. In Proceedings of the OCEANS 2015—MTS/IEEE, Washington, DC, USA, 19–22 October 2015; pp. 1–10. [Google Scholar]
  285. Giron-Sierra, J.M.; Jimenez, J.F. Using an USV for Automatic Deployment of a Boom Around a Ship: Simulation and Scale Experiment. In Proceedings of the OCEANS 2018 MTS/IEEE, Charleston, NC, USA, 22–25 October 2018; pp. 1–10. [Google Scholar]
  286. Giron-Sierra, J.M.; Gheorghita, A.T.; Angulo, G.; Jimenez, J.F. Towing a boom with two USVs for oil spill recovery: Scaled experimental development. In Proceedings of the 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), Singapore, 10–12 December 2014; pp. 1729–1734. [Google Scholar]
  287. Lopperi, T.; Söderberg, H. Development and Systems Integration of Small Hydrofoiling Robot for Mapping and Sensing; KTH Royal Institute of Technology: Stockholm, Sweden, 2022. [Google Scholar]
  288. Tsai, C.-M.; Lai, Y.-H.; Perng, J.-W.; Tsui, I.-F.; Chung, Y.-J. Design and application of an autonomous surface vehicle with an AI-based sensing capability. In Proceedings of the 2019 IEEE Underwater Technology (UT), Kaohsiung, Taiwan, 16–19 April 2019; pp. 1–4. [Google Scholar]
  289. Abrougui, H.; Nejim, S.; Hachicha, S.; Zaoui, C.; Dallagi, H. Modeling, parameter identification, guidance and control of an unmanned surface vehicle with experimental results. Ocean. Eng. 2021, 241, 110038. [Google Scholar] [CrossRef]
  290. Abrougui, H.; Nejim, S. Autopilot Design for an Unmanned Surface Vehicle Based on Backstepping Integral Technique with Experimental Results. J. Mar. Sci. Appl. 2023, 22, 614–623. [Google Scholar] [CrossRef]
  291. Sauzé, C.; Neal, M. An autonomous sailing robot for ocean observation. In Proceedings of the TAROS 2006, Guildford, UK, 4–6 September 2006; pp. 190–197. [Google Scholar]
  292. Sauzé, C.; Neal, M. Design considerations for sailing robots performing long term autonomous oceanography. In Proceedings of the International Robotic Sailing Conference, Breitenbrunn, Austria, 23–24 May 2008; pp. 21–29. [Google Scholar]
  293. Sauzé, C.; Neal, M. A biologically inspired approach to long term autonomy and survival in sailing robots. In Proceedings of the International Robotic Sailing Conference, Breitenbrunn, Austria, 23–24 May 2008; pp. 6–11. [Google Scholar]
  294. Watson, C.S.; Quincey, D.J.; Carrivick, J.L.; Smith, M.W.; Rowan, A.V.; Richardson, R. Heterogeneous water storage and thermal regime of supraglacial ponds on debris-covered glaciers. Earth Surf. Process. Landf. 2018, 43, 229–241. [Google Scholar] [CrossRef]
  295. Metcalfe, B.; Thomas, B.; Treloar, A.; Rymansaib, Z.; Hunter, A.; Wilson, P. A compact, low-cost unmanned surface vehicle for shallow inshore applications. In Proceedings of the 2017 Intelligent Systems Conference (IntelliSys), London, UK, 7–8 September 2017; pp. 961–968. [Google Scholar]
  296. The Wave Glider; Liquid Robotics: 2023. Available online: (accessed on 15 September 2023).
  297. Manley, J.E.; Willcox, S. The Wave Glider: A persistent platform for ocean science. In Proceedings of the OCEANS’10 IEEE SYDNEY, Sydney, NSW, Australia, 24–27 May 2010; pp. 1–5. [Google Scholar]
  298. Wiggins, S.; Manley, J.E.; Brager, E.; Woolhiser, B. Monitoring marine mammal acoustics using wave glider. In Proceedings of the OCEANS 2010 MTS/IEEE SEATTLE, Seattle, WA, USA, 20–23 September 2010; pp. 1–4. [Google Scholar]
  299. Maqueda, M.M.; Penna, N.; Williams, S.; Foden, P.; Martin, I.; Pugh, J. Water surface height determination with a GPS wave glider: A demonstration in Loch Ness, Scotland. J. Atmos. Ocean. Technol. 2016, 33, 1159–1168. [Google Scholar] [CrossRef]
  300. Penna, N.T.; Morales Maqueda, M.A.; Martin, I.; Guo, J.; Foden, P.R. Sea surface height measurement using a GNSS wave glider. Geophys. Res. Lett. 2018, 45, 5609–5616. [Google Scholar] [CrossRef]
  301. Moh, T.; Jang, N.; Jang, S.; Cho, J.H. Application of a winch-type towed acoustic sensor to a wave-powered unmanned surface vehicle. Def. Sci. J. 2017, 67, 125. [Google Scholar] [CrossRef]
  302. Jha, R. Wave measurement methodology and validation from wave glider unmanned surface vehicles. In Proceedings of the 2018 OCEANS-MTS/IEEE Kobe Techno-Oceans (OTO), Kobe, Japan, 28–31 May 2018; pp. 1–7. [Google Scholar]
  303. Raber, G.T.; Schill, S.R. A low-cost small unmanned surface vehicle (sUSV) for very high-resolution mapping and monitoring of shallow marine habitats. In Proceedings of the Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions, Strasbourg, France, 9–10 September 2019; pp. 14–23. [Google Scholar]
  304. Manley, J.E.; Marsh, A.; Cornforth, W.; Wiseman, C. Evolution of the autonomous surface craft AutoCat. In Proceedings of the OCEANS 2000 MTS/IEEE conference and exhibition. Conference proceedings (Cat. No. 00CH37158), Providence, RI, USA, 11–14 September 2000; pp. 403–408. [Google Scholar]
  305. Thompson, N.T.; Whitworth, P.R.; Matveev, K.I. Development of small-scale unmanned hydrofoil boats. J. Unmanned Veh. Syst. 2020, 9, 21–32. [Google Scholar] [CrossRef]
  306. Curcio, J.; Leonard, J.; Patrikalakis, A. SCOUT-a low cost autonomous surface platform for research in cooperative autonomy. In Proceedings of the OCEANS 2005 MTS/IEEE, Washington, DC, USA, 17–23 September 2005; pp. 725–729. [Google Scholar]
  307. Curcio, J.; Schneider, T.; Benjamin, M.; Patrikalakis, A. Autonomous surface craft provide flexibility to remote adaptive oceanographic sampling and modeling. In Proceedings of the OCEANS 2008, Quebec City, QC, Canada, 15–18 September 2008; pp. 1–7. [Google Scholar]
  308. Valada, A.; Velagapudi, P.; Kannan, B.; Tomaszewski, C.; Kantor, G.; Scerri, P. Development of a low cost multi-robot autonomous marine surface platform. In Proceedings of the Field and Service Robotics: Results of the 8th International Conference; Springer: Berlin/Heidelberg, Germany, 2014; pp. 643–658. [Google Scholar]
  309. El-Gaaly, T.; Tomaszewski, C.; Valada, A.; Velagapudi, P.; Kannan, B.; Scerri, P. Visual obstacle avoidance for autonomous watercraft using smartphones. In Proceedings of the AAMAS 13 Workshop on Autonomous Robots and Multirobot Systems, St. Paul, MN, USA, 6–7 May 2013. [Google Scholar]
  310. Valada, A.; Tomaszewski, C.; Kannan, B.; Velagapudi, P.; Kantor, G.; Scerri, P. An intelligent approach to hysteresis compensation while sampling using a fleet of autonomous watercraft. In Proceedings of the Intelligent Robotics and Applications: 5th International Conference, ICIRA 2012, Montreal, QC, Canada, Proceedings; 3–5 October 2012; Part II 5. pp. 472–485. [Google Scholar]
  311. Azevedo, D.; Beltram, S.; DelVecchio, G.; Hopner, B. MARV: Marine Autonomous Research Vessel. Bachelor Thesis, Clara University, Santa Clara, CA, USA, 2016. [Google Scholar]
  312. Miller, P.; Beal, B.; Capron, C.; Gawboy, R.; Mallory, P.; Ness, C.; Petrosik, R.; Pryne, C.; Murphy, T.; Spears, H. Increasing performance and added capabilities of usna sail-powered autonomous surface vessels (asv). In Proceedings of the International Robotic Sailing Conference, Kingston, ON, Canada, 6–10 June 2010. [Google Scholar]
  313. Miller, P.H.; Hamlet, M.; Rossman, J. Continuous Improvements to USNA SailBots for Inshore Racing and Offshore Voyaging; Springer: Berlin/Heidelberg, Germany, 2013; pp. 49–60. [Google Scholar]
  314. Miller, P.; Beeler, A.; Cayaban, B.; Dalton, M.; Fach, C.; Link, C.; MacArthur, J.; Urmenita, J.; Medina, R.Y. An easy-to-build, low-cost, high-performance SailBot. In Robotic Sailing 2014: Proceedings of the 7th International Robotic Sailing Conference; Springer: Berlin/Heidelberg, Germany, 2015; pp. 3–16. [Google Scholar]
  315. Powers, C.; Hanlon, R.; Schmale III, D.G. Remote collection of microorganisms at two depths in a freshwater lake using an unmanned surface vehicle (USV). PeerJ 2018, 6, e4290. [Google Scholar] [CrossRef]
  316. Casper, A.F.; Dixon, B.; Steimle, E.T.; Hall, M.L.; Conmy, R.N. Scales of heterogeneity of water quality in rivers: Insights from high resolution maps based on integrated geospatial, sensor and ROV technologies. Appl. Geogr. 2012, 32, 455–464. [Google Scholar] [CrossRef]
  317. Casper, A.F.; Steimle, E.; Hall, M.; Dixon, B. Combined GIS and ROV technologies improve characterization of water quality in Coastal Rivers of the Gulf of Mexico. In Proceedings of the OCEANS 2009, Bremen, Germany, 11–14 May 2009; pp. 1–9. [Google Scholar]
  318. Tran, N.-H.; Pham, Q.-H.; Lee, J.-H.; Choi, H.-S. VIAM-USV2000: An Unmanned Surface Vessel with Novel Autonomous Capabilities in Confined Riverine Environments. Machines 2021, 9, 133. [Google Scholar] [CrossRef]
  319. Tran, N.-H.; Pham, N.-N.-T. Design Adaptive Controller and Guidance System of an Unmanned Surface Vehicle for Environmental Monitoring Applications. In Proceedings of the 2018 4th International Conference on Green Technology and Sustainable Development (GTSD), Ho Chi Minh City, Vietnam, 23–24 November 2018; pp. 615–620. [Google Scholar]
Figure 1. The methodological approach.
Figure 1. The methodological approach.
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Figure 2. The small USVs included based on the first year of appearance in publications/references (including those published by 30 September 2023).
Figure 2. The small USVs included based on the first year of appearance in publications/references (including those published by 30 September 2023).
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Figure 3. Identified very small USV number per country.
Figure 3. Identified very small USV number per country.
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Figure 4. The most frequently employed hull types in very small USVs.
Figure 4. The most frequently employed hull types in very small USVs.
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Figure 5. Investigated authors co-authorship network using full-counting method.
Figure 5. Investigated authors co-authorship network using full-counting method.
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Figure 6. The terms map generated using title and abstract information.
Figure 6. The terms map generated using title and abstract information.
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Figure 7. Exploitation of identified small USVs per category.
Figure 7. Exploitation of identified small USVs per category.
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Figure 8. Distribution of the number of publications associated with each USV.
Figure 8. Distribution of the number of publications associated with each USV.
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Bolbot, V.; Sandru, A.; Saarniniemi, T.; Puolakka, O.; Kujala, P.; Valdez Banda, O.A. Small Unmanned Surface Vessels—A Review and Critical Analysis of Relations to Safety and Safety Assurance of Larger Autonomous Ships. J. Mar. Sci. Eng. 2023, 11, 2387.

AMA Style

Bolbot V, Sandru A, Saarniniemi T, Puolakka O, Kujala P, Valdez Banda OA. Small Unmanned Surface Vessels—A Review and Critical Analysis of Relations to Safety and Safety Assurance of Larger Autonomous Ships. Journal of Marine Science and Engineering. 2023; 11(12):2387.

Chicago/Turabian Style

Bolbot, Victor, Andrei Sandru, Ture Saarniniemi, Otto Puolakka, Pentti Kujala, and Osiris A. Valdez Banda. 2023. "Small Unmanned Surface Vessels—A Review and Critical Analysis of Relations to Safety and Safety Assurance of Larger Autonomous Ships" Journal of Marine Science and Engineering 11, no. 12: 2387.

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