Challenges in the Guidance, Navigation and Control of Autonomous and Transport Vehicles

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 25552

Special Issue Editors

Department of Engineering, University of Leicester, Leicester LE1 7RH, UK
Interests: fault tolerance; aircraft control; estimation methods; FDIR; UAV; guidance
Special Issues, Collections and Topics in MDPI journals
Department of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK
Interests: control theory; mechatronics; advanced control theory; robotics; system modeling; automation & robotics; systems dynamics; automation; mobile robotics; systems engineering
Special Issues, Collections and Topics in MDPI journals
Department of Industrial Engineering, Bologna University, 40126 Bologna BO, Italy
Interests: flight mechanics; control, UAV; spacecraft; autonomous aircraft; recovery from actuator failures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, transportation systems have considerably evolved in terms of safety, resulting in increased demands on autonomy, performance and energy efficiency. Some of the main research challenges to meet these requirements are rooted in the development of safe and efficient guidance, navigation and control (GNC) systems for different types of transportation vehicles, ranging from mobile robots to automotive and aerospace vehicles, including manned aircraft, different sizes of unmanned aerial vehicles (UAV), and spacecraft. Increasingly autonomous vehicles will require advanced sense-and-avoid technologies and algorithms, the ability to autonomously handle constraints and recover from faults, and an efficient combination of manned and automated control systems. Optimal and robust navigation and control systems are required to meet the challenging performance and energy requirements of the modern day, with computational demand rocketing in increasingly complex vehicle systems. System identification and adaptive control methods are also needed to allow ground and air vehicles to adapt their control algorithms to changes in model parameters. Efficient methods are also needed to combine pilot commands and automation under constraints; for example, in aircraft where flight envelope protection increasingly accounts for pilot handling requirements. Increased levels of autonomy will also require advanced multimode and multiple-input multiple-output (MIMO) navigation and control system architectures, including hierarchical mode-switching control strategies, and sensor fusion-based navigation algorithms. Computationally efficient optimal and artificial intelligence-based methods are also increasingly employed for autonomous vehicle path planning and following in increasingly challenging environments.

This Special Issue will, therefore, bring together papers which describe recent advances in guidance, navigation and control systems for a large range of transportation and autonomous vehicles. Papers with theoretical, simulation and practical experimental results in this field are all encouraged.

Dr. Nadjim Horri
Prof. Dr. William Holderbaum
Dr. Fabrizio Giulietti
Guest Editors

Manuscript Submission Information

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Keywords

  • autonomous
  • aircraft
  • automotive
  • robot
  • guidance
  • navigation
  • control
  • optimal
  • robust
  • adaptive
  • system identification
  • constraints

Published Papers (18 papers)

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23 pages, 9855 KiB  
Article
Global Path Planning for Differential Drive Mobile Robots Based on Improved BSGA* Algorithm
Appl. Sci. 2023, 13(20), 11290; https://doi.org/10.3390/app132011290 - 14 Oct 2023
Viewed by 847
Abstract
The global path planner is an important part of the navigation system for autonomous differential drive mobile robots (DDMRs). Aiming at the problems such as long calculation time, large number of search nodes, and poor smoothness of path when A* is applied to [...] Read more.
The global path planner is an important part of the navigation system for autonomous differential drive mobile robots (DDMRs). Aiming at the problems such as long calculation time, large number of search nodes, and poor smoothness of path when A* is applied to global path planning, this study proposes an improved bidirectional search Gaussian-A* (BSGA*) algorithm. First, the Gaussian function is introduced to realize the dynamic weighting of the heuristic function, which reduces the calculation time. Secondly, the bidirectional search (BS) structure is adopted to solve the problem of nodes’ repeated search when there are large obstacles between the starting point and the target point. Finally, a multi-layer turning point filter strategy is proposed to further smooth the path. In order to verify the performance of the improved BSGA* algorithm, experiments are carried out in simulation environments with the size of 15 × 15 and 30 × 30, respectively, and compared with the five common global path planning algorithms including ant colony optimization (ACO), D* lite algorithm, and genetic algorithm (GA). The results show that the improved BSGA* algorithm has the lowest calculation time and generates the shortest and smoothest path in the same environment. Finally, the program of the improved BSGA* algorithm is embedded into the LEO ROS mobile robot and two different real environments were built for experimental verification. By comparing with the A* algorithm, Dijkstra algorithm, ACO, D* lite algorithm, and GA, the results show that the improved BSGA* algorithm not only outperforms the above five algorithms in terms of calculation time, length, and total turning angle of the generated paths, but also consumes the least time when DDMR drives along the generated paths. Full article
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32 pages, 2757 KiB  
Article
A Generic Framework for Enhancing Autonomous Driving Accuracy through Multimodal Data Fusion
Appl. Sci. 2023, 13(19), 10749; https://doi.org/10.3390/app131910749 - 27 Sep 2023
Cited by 1 | Viewed by 682
Abstract
Higher-level autonomous driving necessitates the best possible execution of important moves under all conditions. Most of the accidents in recent years caused by the AVs launched by leading automobile manufacturers are due to inadequate decision-making, which is a result of their poor perceivance [...] Read more.
Higher-level autonomous driving necessitates the best possible execution of important moves under all conditions. Most of the accidents in recent years caused by the AVs launched by leading automobile manufacturers are due to inadequate decision-making, which is a result of their poor perceivance of environmental information. In today’s technology-bound scenarios, versatile sensors are used by AVs to collect environmental information. Due to various technical and natural calamities, the environmental information acquired by the sensors may not be complete and clear, due to which the AVs may misinterpret the information in a different context, leading to inadequate decision-making, which may then lead to fatal accidents. To overcome this drawback, effective preprocessing of raw sensory data is a mandatory task. Pre-processing the sensory data involves two vital tasks, namely data cleaning and data fusion. Since the raw sensory data are complex and exhibit multimodal characteristics, more emphasis is given to data preprocessing. Since more innovative models have been proposed for data cleaning, this study focused on data fusion. In particular, this study proposed a generic data fusion engine, which classifies different formats of sensory data and fuses them accordingly to improve accuracy. This study proposed a generic framework to fuse the text, image, and audio data. In the first stage of this research, an innovative hybrid model was proposed to fuse multispectral image and video data. Simple and efficient models to extract the salient image features were also proposed. The hybrid image fusion model that was proposed did not yield satisfactory outcomes when combining 3D point cloud data, and its performance declined when evaluating large datasets. To address this issue, the study expanded by introducing an advanced generative adversarial network (GAN) to transform the hybrid image fusion model into a machine learning model capable of handling substantial datasets. Additionally, customized kernel functions were suggested to fuse 3D point cloud data effectively. The performance of the proposed models was assessed using standard metrics and datasets, comparing them with existing popular models. The results revealed that the proposed image fusion model outperformed the other models. Full article
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19 pages, 5274 KiB  
Article
Torque Vectoring Control Strategies Comparison for Hybrid Vehicles with Two Rear Electric Motors
Appl. Sci. 2023, 13(14), 8109; https://doi.org/10.3390/app13148109 - 12 Jul 2023
Cited by 4 | Viewed by 755
Abstract
In today’s automotive industry, electrification is a major trend. In-wheel electric motors are among the most promising technologies yet to be fully developed. Indeed, the presence of multiple in-wheel motors acting as independent actuators allows for the implementation of innovative active systems and [...] Read more.
In today’s automotive industry, electrification is a major trend. In-wheel electric motors are among the most promising technologies yet to be fully developed. Indeed, the presence of multiple in-wheel motors acting as independent actuators allows for the implementation of innovative active systems and control strategies. This paper analyzes different design possibilities for a torque vectoring system applied to an originally compact front-wheel drive hybrid electric vehicle with one internal combustion engine for the front axle and two added electric motors integrated in the wheels of the rear axle. A 14 degrees of freedom vehicle model is present o accurately reproduce the nonlinearities of vehicle dynamic phenomena and exploited to obtain high-fidelity numerical simulation results. Different control methods are compared, a PID, an LQR, and four different sliding mode control strategies. All controllers achieve sufficiently good results in terms of lateral dynamics compared with the basic hybrid version. The various aspects and features of the different strategies are analyzed and discussed. Chattering reduction strategies are developed to improve the performance of sliding mode controllers. For a complete overview, control systems are compared using a performance factor that weighs control accuracy and effort in different driving maneuvers, i.e., ramp and step steering maneuvers performed under quite different conditions ranging up to the limits. Full article
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22 pages, 13665 KiB  
Article
GNSS Signal Quality in Forest Stands for Off-Road Vehicle Navigation
Appl. Sci. 2023, 13(10), 6142; https://doi.org/10.3390/app13106142 - 17 May 2023
Cited by 1 | Viewed by 1023
Abstract
One of the basic possibilities of orientation in forest stands is the use of global navigation satellite systems (GNSS). Today, these systems are used for pedestrian orientation and also for off-road vehicle navigation. This article presents the results of research aimed at measuring [...] Read more.
One of the basic possibilities of orientation in forest stands is the use of global navigation satellite systems (GNSS). Today, these systems are used for pedestrian orientation and also for off-road vehicle navigation. This article presents the results of research aimed at measuring the quality of GNSS signal in different types of coniferous and deciduous vegetation for the purpose of optimizing the navigation of off-road vehicles. To determine the structure (density) of the forest stand, tachymetry was chosen as the reference method. The Trimble Geo 7X cm edition device with Tornado for 7X antenna devices using real time VRS (virtual reference station) method was used to measure GNSS signal quality. This article presents the results of recorded numbers of GNSS satellites (GPS, GLONASS, Galileo and BeiDou) during the driving of a terrain vehicle in two different forest locations. Significant presented results include the deviations of vehicle positions determined by GNSS from tachymetrically precisely measured and marked routes along which the vehicle was moving. The authors of the article focused on the accuracy of determining the position of the vehicle using GNNS, as the most commonly used device for off-road vehicle navigation. The measurement results confirmed the assumption that the accuracy of positioning was better in deciduous forest than in coniferous (spruce) or mixed vegetation. This research was purposefully focused on the possibilities of navigation of military vehicles, but the achieved results can also be applied to the navigation of forestry, rescue and other types of off-road vehicles. Full article
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19 pages, 4821 KiB  
Article
High-Efficiency Navigation of Nonholonomic Mobile Robots Based on Improved Hybrid A* Algorithm
Appl. Sci. 2023, 13(10), 6141; https://doi.org/10.3390/app13106141 - 17 May 2023
Cited by 3 | Viewed by 1455
Abstract
With the development of automation technologies, autonomous robots are increasingly used in many important applications. However, precise self-navigation and accurate path planning remain a significant challenge, particularly for the robots operating in complex circumstances such as city centers. In this paper, a nonholonomically [...] Read more.
With the development of automation technologies, autonomous robots are increasingly used in many important applications. However, precise self-navigation and accurate path planning remain a significant challenge, particularly for the robots operating in complex circumstances such as city centers. In this paper, a nonholonomically constrained robot with high-precision navigation and path planning capability was designed based on the Robot Operating System (ROS), and an improved hybrid A* algorithm was developed to increase the processing efficiency and accuracy of the global path planning and navigation of the robot. The performance and effectiveness of the algorithm were evaluated by using randomly constructed maps in MATLAB and validated in a practical circumstance. Local path planning and obstacle avoidance were carried out based on the model predictive control (MPC) theory. Compared with the conventional A* + DWA (dynamic window approach) method, the average searching time was reduced by 12.62~24.5%, and the average search length was reduced by 9.25~9.5%. In practical navigating tests, the average search time was reduced by 18~24%, and the average search length was reduced by 10.3~12%, while the overall path was smoother. The results demonstrate that the improved algorithm can enable precise and efficient navigation and path planning of the robot in complex circumstances. Full article
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15 pages, 4776 KiB  
Article
Robust Autonomous Vehicle Computer-Vision-Based Localization in Challenging Environmental Conditions
Appl. Sci. 2023, 13(9), 5735; https://doi.org/10.3390/app13095735 - 06 May 2023
Viewed by 1068
Abstract
In this paper, we present a novel autonomous vehicle (AV) localization design and its implementation, which we recommend to employ in challenging navigation conditions with a poor quality of the satellite navigation system signals and computer vision images. In the case when the [...] Read more.
In this paper, we present a novel autonomous vehicle (AV) localization design and its implementation, which we recommend to employ in challenging navigation conditions with a poor quality of the satellite navigation system signals and computer vision images. In the case when the GPS signal becomes unstable, other auxiliary navigation systems, such as computer-vision-based positioning, are employed for more accurate localization and mapping. However, the quality of data obtained from AV’s sensors might be deteriorated by the extreme environmental conditions too, which infinitely leads to the decrease in navigation performance. To verify our computer-vision-based localization system design, we considered the Arctic region use case, which poses additional challenges for the AV’s navigation and might employ artificial visual landmarks for improving the localization quality, which we used for the computer vision training. We further enhanced our data by applying affine transformations to increase its diversity. We selected YOLOv4 image detection architecture for our system design, as it demonstrated the highest performance in our experiments. For the computational platform, we employed a Nvidia Jetson AGX Xavier device, as it is well known and widely used in robotic and AV computer vision, as well as deep learning applications. Our empirical study showed that the proposed computer vision system that was further trained on the dataset enhanced by affine transformations became robust regarding image quality degradation caused by extreme environmental conditions. It was effectively able to detect and recognize images of artificial visual landmarks captured in the extreme Arctic region’s conditions. The developed system can be integrated into vehicle navigation facilities to improve their effectiveness and efficiency and to prevent possible navigation performance deterioration. Full article
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15 pages, 502 KiB  
Article
Design and Implementation of an Energy-Efficient Vehicle Platoon Control Algorithm Using Prescribed Performance and Extremum Seeking Control
Appl. Sci. 2023, 13(9), 5650; https://doi.org/10.3390/app13095650 - 04 May 2023
Viewed by 1246
Abstract
Platooning has emerged as a promising approach to enhancing the fuel efficiency of vehicles, but determining the inter-vehicular distance that achieves the minimum consumption remains a challenge. In this article, an algorithm is proposed that employs extremum seeking control integrated with the prescribed [...] Read more.
Platooning has emerged as a promising approach to enhancing the fuel efficiency of vehicles, but determining the inter-vehicular distance that achieves the minimum consumption remains a challenge. In this article, an algorithm is proposed that employs extremum seeking control integrated with the prescribed performance control technique to find the optimal inter-vehicular distance. The algorithm utilizes the predecessor-following architecture to track the desired distance while minimizing the estimated aerodynamic drag coefficient to seek the optimal value. To estimate the coefficient, an observer is designed. Simulation results are presented to demonstrate the effectiveness of the approach. The proposed algorithm exhibits a significant improvement over existing methods that do not incorporate prescribed performance. Consequently, our scheme provides a valuable contribution to the field of platooning and paves the way for future research directions. Full article
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23 pages, 1579 KiB  
Article
Longitudinal Control Strategy for Connected Electric Vehicle with Regenerative Braking in Eco-Approach and Departure
Appl. Sci. 2023, 13(8), 5089; https://doi.org/10.3390/app13085089 - 19 Apr 2023
Cited by 3 | Viewed by 1283
Abstract
The development of more sustainable urban transportation is prompting the need for better energy management techniques. Connected electric vehicles can take advantage of environmental information regarding the status of traffic lights. In this context, eco-approach and departure methods have been proposed in the [...] Read more.
The development of more sustainable urban transportation is prompting the need for better energy management techniques. Connected electric vehicles can take advantage of environmental information regarding the status of traffic lights. In this context, eco-approach and departure methods have been proposed in the literature. Integrating these methods with regenerative braking allows for safe, power-efficient navigation through intersections and crossroad layouts. This paper proposes rule- and fuzzy inference system-based strategies for a coupled eco-approach and departure regenerative braking system. This analysis is carried out through a numerical simulator based on a three-degree-of-freedom connected electric vehicle model. The powertrain is represented by a realistic power loss map in motoring and regenerative quadrants. The simulations aim to compare both longitudinal navigation strategies by means of relevant metrics: power, efficiency, comfort, and usage duty cycle in motor and generator modes. Numerical results show that the vehicle is able to yield safe navigation while focusing on energy regeneration through different navigation conditions. Full article
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19 pages, 6899 KiB  
Article
A Study on Longitudinal Motion Scenario Design for Verification of Advanced Driver Assistance Systems and Autonomous Driving Systems
Appl. Sci. 2023, 13(2), 716; https://doi.org/10.3390/app13020716 - 04 Jan 2023
Cited by 1 | Viewed by 1245
Abstract
This paper proposes a test scenario design method that reflects the longitudinal characteristics of reality for effective verification of advanced driver assistance systems (ADAS) and autonomous driving systems (ADS). Since the target systems interact with the external environment differently from the existing vehicle [...] Read more.
This paper proposes a test scenario design method that reflects the longitudinal characteristics of reality for effective verification of advanced driver assistance systems (ADAS) and autonomous driving systems (ADS). Since the target systems interact with the external environment differently from the existing vehicle control system, realistic and various verification scenarios are required for verification. The proposed method consists of a vehicle model for simulating the vehicle behavior and a driver model to actively respond to the driving environment. In particular, the driver model used a model predictive control (MPC) algorithm to reflect the characteristic of human drivers. The longitudinal driving characteristics of human drivers were derived through a large-scale driving database analysis and considered in the driver model. The proposed method was compared with an existing car-following model using computer simulations. It was confirmed that its longitudinal driving behavior is similar to that of human drivers and that various scenarios can be designed by changing the model parameters. Full article
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18 pages, 6260 KiB  
Article
A New HEV Power Distribution Algorithm Using Nonlinear Programming
Appl. Sci. 2022, 12(24), 12724; https://doi.org/10.3390/app122412724 - 12 Dec 2022
Cited by 3 | Viewed by 1065
Abstract
An equivalent consumption minimization strategy (ECMS) is one of the most powerful and practical ways to improve the fuel efficiency of hybrid electric vehicles (HEVs). In an ECMS, it is important to determine the optimal equivalent factor to reach a global optimal solution. [...] Read more.
An equivalent consumption minimization strategy (ECMS) is one of the most powerful and practical ways to improve the fuel efficiency of hybrid electric vehicles (HEVs). In an ECMS, it is important to determine the optimal equivalent factor to reach a global optimal solution. The optimal equivalent factor is determined by driving conditions. Previous studies have used an adaptive ECMS (A-ECMS) to determine the appropriate equivalent factor according to changing driving conditions. An A-ECMS adjusts the equivalent factor by controlling the battery’s state of charge (SOC) to follow a reference SOC trajectory. It is therefore critical to identify a reference SOC trajectory that reflects real-world driving conditions. These conditions, which are composed of the HEV’s nonlinear dynamics and complex constraints, can be formulated into a nonlinear optimal control problem (NOCP). Here, we propose applying nonlinear programming (NLP) to an A-ECMS. The NLP-based ECMS algorithm can be divided into two parts: the use of an NLP to solve an NOCP to obtain the reference SOC trajectory and the application of an NLP solution (the result of the first part) to an A-ECMS. Simulation results demonstrate that the proposed NLP-based ECMS closely resembles a global optimal solution for dynamic programming in a relatively brief calculation time. Full article
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15 pages, 3713 KiB  
Article
Cooperative Roundabout Control Strategy for Connected and Autonomous Vehicles
Appl. Sci. 2022, 12(24), 12678; https://doi.org/10.3390/app122412678 - 10 Dec 2022
Cited by 5 | Viewed by 954
Abstract
Intersections in the urban network are potential sources of traffic flow inefficiency. Existing intersection control mostly adopts the “cross” flow pattern model, while the use of the roundabout circular flow pattern is rather sparse. Connected and autonomous vehicle (CAV) technologies can enable roundabouts [...] Read more.
Intersections in the urban network are potential sources of traffic flow inefficiency. Existing intersection control mostly adopts the “cross” flow pattern model, while the use of the roundabout circular flow pattern is rather sparse. Connected and autonomous vehicle (CAV) technologies can enable roundabouts to better compete with traditional intersection designs in terms of performance. This study proposes a roundabout control strategy for CAVs to enhance intersection performance while ensuring vehicle safety. A hierarchical framework is developed to decouple the flow-level performance objective and vehicle-level safety constraints to achieve computational tractability for real-time applications. It entails developing a roundabout flow control model to optimize merge-in flows, a merge-in decision model to generate vehicle passing sequence from the optimal flows, and a virtual platoon control model to achieve safe and stable vehicle operations in a circular roundabout platoon. The performance of the proposed roundabout control strategy is illustrated through numerical studies and compared to existing intersection control methods. Its stability and safety characteristics are also demonstrated. Full article
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20 pages, 2518 KiB  
Article
UGAVs-MDVR: A Cluster-Based Multicast Routing Protocol for Unmanned Ground and Aerial Vehicles Communication in VANET
Appl. Sci. 2022, 12(23), 11995; https://doi.org/10.3390/app122311995 - 24 Nov 2022
Cited by 3 | Viewed by 1374
Abstract
Unmanned ground vehicles (UGVs) are becoming the foremost part of rescue teams for protecting human lives from severe disasters and reducing human casualties by informing them about the risks ahead, such as mine detection and clearance. In mine detection, a centralized system is [...] Read more.
Unmanned ground vehicles (UGVs) are becoming the foremost part of rescue teams for protecting human lives from severe disasters and reducing human casualties by informing them about the risks ahead, such as mine detection and clearance. In mine detection, a centralized system is required so that the UGVs can communicate with each other efficiently to disseminate the mine detection messages (MDMs) to incoming vehicles of military and civilians. Therefore, in this piece of research, a novel unmanned ground and aerial vehicle (UGAV)-based mine-detection-vehicle routing (MDVR) protocol has been proposed, mainly for the mine detection and clearance teams using a vehicular ad hoc network (VANET). The protocol disseminates the MDMs using UGVs and unmanned aerial vehicles (UAVs) in combination to overcome the limitations of only inter-UGV communication. The proposed protocol performs cluster-based multicast communication in real time using UAVs so that the dynamic mobility of UGVs cannot affect the performance of MDM dissemination. Hence, the proposed scheme is adaptable because any failure in message delivery can cause a high level of destruction. The proposed cluster-based scheme can adapt to any real-time scenario by introducing the level-based cluster-head election scheme (LBCHE), which works concerning its assigned priority for reducing the delay incurred in MDMs dissemination. The simulation of the proposed protocol in the network simulator (NS) shows that the overhead and delay are reduced in MDMs dissemination. At the same time, the throughput, packet delivery ratio, and stability increased compared to the other competing protocols. Full article
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21 pages, 4318 KiB  
Article
Application and Assessment of Cooperative Localization in Three-Dimensional Vehicle Networks
Appl. Sci. 2022, 12(22), 11805; https://doi.org/10.3390/app122211805 - 20 Nov 2022
Cited by 1 | Viewed by 1037
Abstract
The trajectory planning and control of multi-agent systems requires accurate localization, which may not be possible when GPS signals and fixed features required for SLAM are not available. Cooperative Localization (CL) in multi-agent systems offers a short-term solution that may significantly improve vehicle [...] Read more.
The trajectory planning and control of multi-agent systems requires accurate localization, which may not be possible when GPS signals and fixed features required for SLAM are not available. Cooperative Localization (CL) in multi-agent systems offers a short-term solution that may significantly improve vehicle pose estimation. CL algorithms have been mainly developed and assessed for planar mobile robot networks due to complexities and singularities in three-dimensional (3D) motion. In this paper, we develop the required singularity-free equations and apply and assess an EKF-based CL for 3D vehicle networks. We assess the performance of CL with respect to the number of simultaneous and redundant measurements. We further assess CL performance with only relative position measurements available. Finally, experiments are performed to validate the proposed algorithms. We further investigate the effect of absolute position measurements in CL. Conclusions: Cooperative localization is an effective method when applied to 3D vehicle networks. However, CL does not improve localization with only relative position measurements, and thus previously reported results for 2D vehicle models were only effective due to relative orientation measurements. Absolute measurement reduces the overall localization errors much more significantly when there has been CL with prior relative position measurements. Full article
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15 pages, 3478 KiB  
Article
Research on Data Fusion of Positioning System with a Fault Detection Mechanism for Autonomous Vehicles
Appl. Sci. 2022, 12(22), 11339; https://doi.org/10.3390/app122211339 - 08 Nov 2022
Viewed by 979
Abstract
A positioning system provides useful positioning information for applications. The ability to maintain high-quality positioning information in real time is crucial. Autonomous vehicles require positioning information to plan control strategies. Inadequate positioning accuracy affects control and safety. This article explores the effect of [...] Read more.
A positioning system provides useful positioning information for applications. The ability to maintain high-quality positioning information in real time is crucial. Autonomous vehicles require positioning information to plan control strategies. Inadequate positioning accuracy affects control and safety. This article explores the effect of an error detection and correction method on coordinate information fusion. This method can be used to efficiently correct positioning errors in the presence of faults. The fault detection mechanism is not limited to positioning systems with certain types of sensor applications. and it has certain flexibility in various applications. This positioning system detection method uses more than two positioning systems. Primarily, one positioning system is used to monitor the other. When the error of the positioning system differs excessively from the system setting value, the positioning fusion algorithm will make appropriate adjustments to output the corrected coordinate value. Therefore, this method integrates different positioning technologies, including a global position system (GPS), simultaneous localization and mapping (SLAM), inertial measurement units (IMUs), and mileage meters. Autonomous vehicles already have the required sensors deployed, and our method can be implemented at no additional cost. We integrated two positioning systems to support the positioning applications and designed a fault detection method to detect errors in the positioning system. This allowed the system to have some reference information by which to correct the coordinate information. According to the experimental findings, positioning information errors were corrected at a rate of 89.8%. The positioning system with the added detection function was able to effectively filter the data without too many errors. Additionally, the results showed that this method can effectively correct positioning errors. The results indicate that adding an error detection mechanism can effectively improve the accuracy of coordinate data fusion. Positioning systems with an error detection function can thus improve applications’ performance. Full article
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25 pages, 6031 KiB  
Article
Jam Mitigation for Autonomous Convoys via Behavior-Based Robotics
Appl. Sci. 2022, 12(19), 9863; https://doi.org/10.3390/app12199863 - 30 Sep 2022
Cited by 4 | Viewed by 1484
Abstract
Autonomous ground vehicle convoys heavily rely on wireless communications to perform leader-follower operations, which make them particularly vulnerable to denial-of-service attacks such as jamming. To mitigate the effects of jamming on autonomous convoys, this paper proposes a behavior-based architecture, called the Behavior Manager, [...] Read more.
Autonomous ground vehicle convoys heavily rely on wireless communications to perform leader-follower operations, which make them particularly vulnerable to denial-of-service attacks such as jamming. To mitigate the effects of jamming on autonomous convoys, this paper proposes a behavior-based architecture, called the Behavior Manager, that utilizes layered costmaps and vector field histogram motion planning to implement motor schema behaviors. Using our proposed Behavior Manager, multiple behaviors can be created to form a convoy controller assemblage capable of continuing convoy operations while under a jamming attack. To measure the performance of our proposed solution to jammed autonomous convoying, simulated convoy runs are performed on multiple path plans under different types of jamming attacks, using both the assemblage and a basic delayed follower convoy controller. Extensive simulation results demonstrated that our proposed solution, the Behavior Manager, can be leveraged to dramatically improve the robustness of autonomous convoys when faced with jamming attacks and can be further extended due to its modular nature to combat other types of attacks through the development of additional behaviors and assemblages. When comparing the performance of the Behavior Manager convoy to that of the basic convoy controller, improvements were seen across all jammer types and path plans, ranging from 13.33% to 86.61% reductions in path error. Full article
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11 pages, 3108 KiB  
Article
Effect of Onboard Training for Improvement of Navigation Skill under the Simulated Navigation Environment for Maritime Autonomous Surface Ship Operation Training
Appl. Sci. 2022, 12(18), 9300; https://doi.org/10.3390/app12189300 - 16 Sep 2022
Cited by 2 | Viewed by 1843
Abstract
As the technology of the maritime autonomous surface ship (MASS) systems has geared toward autonomy, the importance of human operations in the shore control center (SCC) has gained in significance. Accordingly, the effects of the training method, including the traditional and new remote [...] Read more.
As the technology of the maritime autonomous surface ship (MASS) systems has geared toward autonomy, the importance of human operations in the shore control center (SCC) has gained in significance. Accordingly, the effects of the training method, including the traditional and new remote operator training methods have to be investigated in terms of MASS navigation safety. Therefore, this study conducted a comparative analysis to prove the effect of onboard training. The findings include the execution of a simulated navigation experiment, the extraction of rudder steering-related features, selection of significant features, and comparative analysis with network graph visualization. The separate results obtained from the “untrained” group and “trained” group were exhibited as the purpose of research for the effect of onboard training on navigation skills. Then, the authors interpreted the difference in each group allusively in accordance with features considering actual navigation and compared groups using descriptive statistics. Consequently, this study emphasized the importance of proving the effect of training before the new training technologies are used to train MASS remote operators in the future. Full article
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12 pages, 2504 KiB  
Article
GNSS/INS Integration Based on Machine Learning LightGBM Model for Vehicle Navigation
Appl. Sci. 2022, 12(11), 5565; https://doi.org/10.3390/app12115565 - 30 May 2022
Cited by 5 | Viewed by 2501
Abstract
To solve the problem of data accuracy degradation of vehicle GNSS/INS integrated navigation systems when the GNSS signal is unavailable or there is a GNSS outage, this paper improves the existing GNSS/INS integration methodology for land vehicle navigation based on the AI method. [...] Read more.
To solve the problem of data accuracy degradation of vehicle GNSS/INS integrated navigation systems when the GNSS signal is unavailable or there is a GNSS outage, this paper improves the existing GNSS/INS integration methodology for land vehicle navigation based on the AI method. First, a GNSS/INS integration methodology for land vehicle navigation based on position update architecture (PUA) using LightGBM regression for predicting the position of a vehicle during a GNSS outage is presented. It uses LightGBM to model the relationship between INS data and vehicle position changes. On-board INS and GNSS data are collected when the GNSS signal is available and are used to train the PUA-LightGBM model; in the event of a GNSS outage, INS data are used as the input to the PUA-LightGBM to predict the change in vehicle position. Second, a vehicle navigation data acquisition system was designed for model validation. This included a self-developed GNSS/INS integrated navigation system and a Novatel pwrpak7-e1 GNSS/INS integrated navigation system for data acquisition on six road segments. Finally, the collected data were used for machine learning training of the PUA-LightGBM model and the existing PUA-RandomForest model. As a result, the PUA-LightGBM predicts the vehicle position with less error in the event of a GNSS outage and takes less time to train. It was also demonstrated that by allowing the model to be dynamically trained or updated while the vehicle is moving the PUA-LightGBM could adapt perfectly to the predictions of vehicle position changes in different complex road segments. Full article
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Review

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33 pages, 440 KiB  
Review
Challenges and Solutions for Autonomous Ground Robot Scene Understanding and Navigation in Unstructured Outdoor Environments: A Review
Appl. Sci. 2023, 13(17), 9877; https://doi.org/10.3390/app13179877 - 31 Aug 2023
Cited by 3 | Viewed by 2034
Abstract
The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, robots have achieved relatively high levels of autonomy. In more unstructured environments, however, the [...] Read more.
The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, robots have achieved relatively high levels of autonomy. In more unstructured environments, however, the development of fully autonomous mobile robots remains challenging due to the complexity of understanding these environments. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots must understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, rough or smooth terrain, etc.) are required for autonomous robot navigation in unstructured outdoor environments. This paper provides a comprehensive review and critical analysis of these methods in the context of their applications to the problems of robot perception and scene understanding in unstructured environments and the related problems of localisation, environment mapping and path planning. State-of-the-art sensor fusion methods and multimodal scene understanding approaches are also discussed and evaluated within this context. The paper concludes with an in-depth discussion regarding the current state of the autonomous ground robot navigation challenge in unstructured outdoor environments and the most promising future research directions to overcome these challenges. Full article
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