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Deployment and Navigation of Aerial Drones for Surveillance and Monitoring
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Deployment and Navigation of Aerial Drones for Surveillance and Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (15 August 2019) | Viewed by 68795

Special Issue Editor

Prof. Dr. Andrey V. Savkin

E-Mail Website
Guest Editor
School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia
Interests: robot navigation; deployment of drones; unmanned aerial vehicles; control of wireless communication networks; control of power systems; robust control and filtering; hybrid dynamical systems; control engineering; biomedical engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions to a Special Issue of Sensors entitled “Deployment and Navigation of Aerial Drones for Surveillance and Monitoring”. Aerial drones may refer to unmanned aerial vehicles (UAVs), flying robots, or airships in different applications. Their use is rapidly expanding to numerous applications, such as communication, environmental monitoring, rescue operations, policing, surveillance, product deliveries, aerial photography, and agriculture. Sales of commercial drones are expected to grow from 2.5 million drones in 2016 to 7 million in 2020. In surveillance applications, drones are equipped with sensors such as cameras. They fly into the sky and monitor ground objects of interest, such as humans, animals, vehicles, landmarks, and disaster areas. For these applications, the efficient deployment and navigation of aerial drones are critical issues. Advanced methods of navigation and deployment play an important role in achieving the reliable, robust, secure, and cost-effective functioning of UAV networks. Researchers and engineers worldwide are working together to develop novel and efficient tools for the deployment and navigation of networks of aerial drones for monitoring and surveillance. This Special Issue is focused on new developments in the field of placement and navigation of UAVs for surveillance applications.

Potential topics include, but are not limited to, the following:

  • Reactive deployment of aerial drones;
  • Proactive deployment of aerial drones;
  • UAV navigation;
  • Surveillance and following moving objects using UAVs;
  • Networks of UAVs;
  • UAV path planning;
  • Collision avoidance for UAVs;
  • Deployment and control of flying sensor networks;
  • Coverage control in UAV surveillance;
  • Environmental monitoring by UAVs.

Prof. Dr. Andrey V. Savkin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • unmanned aerial vehicles
  • networks of drones
  • deployment of aerial drones
  • aerial surveillance and monitoring
  • navigation of UAVs
  • UAV path planning
  • internet of drones
  • internet of flying robots
  • UAV collision avoidance
  • flying sensor networks

Published Papers (16 papers)

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Research

27 pages, 10475 KiB  
Article
Behavior-Based Control for an Aerial Robotic Swarm in Surveillance Missions
by Pablo Garcia-Aunon, Jaime del Cerro and Antonio Barrientos
Sensors 2019, 19(20), 4584; https://doi.org/10.3390/s19204584 - 21 Oct 2019
Cited by 11 | Viewed by 3120
Abstract
Aerial robotic swarms have shown benefits for performing search and surveillance missions in open spaces in the past. Among other properties, these systems are robust, scalable and adaptable to different scenarios. In this work, we propose a behavior-based algorithm to carry out a [...] Read more.
Aerial robotic swarms have shown benefits for performing search and surveillance missions in open spaces in the past. Among other properties, these systems are robust, scalable and adaptable to different scenarios. In this work, we propose a behavior-based algorithm to carry out a surveillance task in a rectangular area with a flexible number of quadcopters, flying at different speeds. Once the efficiency of the algorithm is quantitatively analyzed, the robustness of the system is demonstrated with 3 different tests: loss of broadcast messages, positioning errors, and failure of half of the agents during the mission. Experiments are carried out in an indoor arena with micro quadcopters to support simulation results. Finally, a case study is proposed to show a realistic implementation in the test bed. Full article
(This article belongs to the Special Issue Deployment and Navigation of Aerial Drones for Surveillance and Monitoring)
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22 pages, 36100 KiB  
Article
A Framework for Automated Acquisition and Processing of As-Built Data with Autonomous Unmanned Aerial Vehicles
by Henk Freimuth and Markus König
Sensors 2019, 19(20), 4513; https://doi.org/10.3390/s19204513 - 17 Oct 2019
Cited by 15 | Viewed by 3555
Abstract
Planning and scheduling in construction heavily depend on current information about the state of construction processes. However, the acquisition process for visual data requires human personnel to take photographs of construction objects. We propose using unmanned aerial vehicle (UAVs) for automated creation of [...] Read more.
Planning and scheduling in construction heavily depend on current information about the state of construction processes. However, the acquisition process for visual data requires human personnel to take photographs of construction objects. We propose using unmanned aerial vehicle (UAVs) for automated creation of images and point cloud data of particular construction objects. The method extracts locations of objects that require inspection from Four Dimensional Building Information Modelling (4D-BIM). With this information at hand viable flight missions around the known structures of the construction site are computed. During flight, the UAV uses stereo cameras to detect and avoid any obstacles that are not known to the model, for example moving humans or machinery. The combination of pre-computed waypoint missions and reactive avoidance ensures deterministic routing from takeoff to landing and operational safety for humans and machines. During flight, an additional software component compares the captured point cloud data with the model data, enabling automatic per-object completion checking or reconstruction. The prototype is developed in the Robot Operating System (ROS) and evaluated in Software-In-The-Loop (SITL) simulations for the sake of being executable on real UAVs. Full article
(This article belongs to the Special Issue Deployment and Navigation of Aerial Drones for Surveillance and Monitoring)
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20 pages, 4835 KiB  
Article
Beehive-Inspired Information Gathering with a Swarm of Autonomous Drones
by Alberto Viseras, Thomas Wiedemann, Christoph Manss, Valentina Karolj, Dmitriy Shutin and Juan Marchal
Sensors 2019, 19(19), 4349; https://doi.org/10.3390/s19194349 - 08 Oct 2019
Cited by 12 | Viewed by 4377
Abstract
This paper presents a beehive-inspired multi-agent drone system for autonomous information collection to support the needs of first responders and emergency teams. The proposed system is designed to be simple, cost-efficient, yet robust and scalable at the same time. It includes several unmanned [...] Read more.
This paper presents a beehive-inspired multi-agent drone system for autonomous information collection to support the needs of first responders and emergency teams. The proposed system is designed to be simple, cost-efficient, yet robust and scalable at the same time. It includes several unmanned aerial vehicles (UAVs) that can be tasked with data collection, and a single control station that acts as a data accumulation and visualization unit. The system also provides a local communication access point for the UAVs to exchange information and coordinate the data collection routes. By avoiding peer-to-peer communication and using proactive collision avoidance and path-planning, the payload weight and per-drone costs can be significantly reduced; the whole concept can be implemented using inexpensive off-the-shelf components. Moreover, the proposed concept can be used with different sensors and types of UAVs. As such, it is suited for local-area operations, but also for large-scale information-gathering scenarios. The paper outlines the details of the system hardware and software design, and discusses experimental results for collecting image information with a set of 4 multirotor UAVs at a small experimental area. The obtained results validate the concept and demonstrate robustness and scalability of the system. Full article
(This article belongs to the Special Issue Deployment and Navigation of Aerial Drones for Surveillance and Monitoring)
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26 pages, 9295 KiB  
Article
Gunshot Airborne Surveillance with Rotary Wing UAV-Embedded Microphone Array
by Felipe Gonçalves Serrenho, José Antonio Apolinário, Jr., António Luiz Lopes Ramos and Rigel Procópio Fernandes
Sensors 2019, 19(19), 4271; https://doi.org/10.3390/s19194271 - 01 Oct 2019
Cited by 8 | Viewed by 4210
Abstract
Unmanned aerial vehicles (UAV) are growing in popularity, and recent technological advances are fostering the development of new applications for these devices. This paper discusses the use of aerial drones as a platform for deploying a gunshot surveillance system based on an array [...] Read more.
Unmanned aerial vehicles (UAV) are growing in popularity, and recent technological advances are fostering the development of new applications for these devices. This paper discusses the use of aerial drones as a platform for deploying a gunshot surveillance system based on an array of microphones. Notwithstanding the difficulties associated with the inherent additive noise from the rotating propellers, this application brings an important advantage: the possibility of estimating the shooter position solely based on the muzzle blast sound, with the support of a digital map of the terrain. This work focuses on direction-of-arrival (DoA) estimation methods applied to audio signals obtained from a microphone array aboard a flying drone. We investigate preprocessing and different DoA estimation techniques in order to obtain the setup that performs better for the application at hand. We use a combination of simulated and actual gunshot signals recorded using a microphone array mounted on a UAV. One of the key insights resulting from the field recordings is the importance of drone positioning, whereby all gunshots recorded in a region outside a cone open from the gun muzzle presented a hit rate close to 96%. Based on experimental results, we claim that reliable bearing estimates can be achieved using a microphone array mounted on a drone. Full article
(This article belongs to the Special Issue Deployment and Navigation of Aerial Drones for Surveillance and Monitoring)
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11 pages, 689 KiB  
Article
Convex Decomposition for a Coverage Path Planning for Autonomous Vehicles: Interior Extension of Edges
by Lasse Damtoft Nielsen, Inkyung Sung and Peter Nielsen
Sensors 2019, 19(19), 4165; https://doi.org/10.3390/s19194165 - 25 Sep 2019
Cited by 36 | Viewed by 5291
Abstract
To cover an area of interest by an autonomous vehicle, such as an Unmanned Aerial Vehicle (UAV), planning a coverage path which guides the unit to cover the area is an essential process. However, coverage path planning is often problematic, especially when the [...] Read more.
To cover an area of interest by an autonomous vehicle, such as an Unmanned Aerial Vehicle (UAV), planning a coverage path which guides the unit to cover the area is an essential process. However, coverage path planning is often problematic, especially when the boundary of the area is complicated and the area contains several obstacles. A common solution for this situation is to decompose the area into disjoint convex sub-polygons and to obtain coverage paths for each sub-polygon using a simple back-and-forth pattern. Aligned with the solution approach, we propose a new convex decomposition method which is simple and applicable to any shape of target area. The proposed method is designed based on the idea that, given an area of interest represented as a polygon, a convex decomposition of the polygon mainly occurs at the points where an interior angle between two edges of the polygon is greater than 180 degrees. The performance of the proposed method is demonstrated by comparison with existing convex decomposition methods using illustrative examples. Full article
(This article belongs to the Special Issue Deployment and Navigation of Aerial Drones for Surveillance and Monitoring)
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21 pages, 21291 KiB  
Article
On-Board Real-Time Trajectory Planning for Fixed Wing Unmanned Aerial Vehicles in Extreme Environments
by Ben Schellenberg, Tom Richardson, Arthur Richards, Robert Clarke and Matt Watson
Sensors 2019, 19(19), 4085; https://doi.org/10.3390/s19194085 - 21 Sep 2019
Cited by 12 | Viewed by 3752
Abstract
A team from the University of Bristol have developed a method of operating fixed wing Unmanned Aerial Vehicles (UAVs) at long-range and high-altitude over Volcán de Fuego in Guatemala for the purposes of volcanic monitoring and ash-sampling. Conventionally, the mission plans must be [...] Read more.
A team from the University of Bristol have developed a method of operating fixed wing Unmanned Aerial Vehicles (UAVs) at long-range and high-altitude over Volcán de Fuego in Guatemala for the purposes of volcanic monitoring and ash-sampling. Conventionally, the mission plans must be carefully designed prior to flight, to cope with altitude gains in excess of 3000 m, reaching 9 km from the ground control station and 4500 m above mean sea level. This means the climb route cannot be modified mid-flight. At these scales, atmospheric conditions change over the course of a flight and so a real-time trajectory planner (RTTP) is desirable, calculating a route on-board the aircraft. This paper presents an RTTP based around a genetic algorithm optimisation running on a Raspberry Pi 3 B+, the first of its kind to be flown on-board a UAV. Four flights are presented, each having calculated a new and valid trajectory on-board, from the ground control station to the summit region of Volcań de Fuego. The RTTP flights are shown to have approximately equivalent efficiency characteristics to conventionally planned missions. This technology is promising for the future of long-range UAV operations and further development is likely to see significant energy and efficiency savings. Full article
(This article belongs to the Special Issue Deployment and Navigation of Aerial Drones for Surveillance and Monitoring)
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22 pages, 1378 KiB  
Article
Autonomous Unmanned Aerial Vehicles in Search and Rescue Missions Using Real-Time Cooperative Model Predictive Control
by Fabio Augusto de Alcantara Andrade, Anthony Reinier Hovenburg, Luciano Netto de Lima, Christopher Dahlin Rodin, Tor Arne Johansen, Rune Storvold, Carlos Alberto Moraes Correia and Diego Barreto Haddad
Sensors 2019, 19(19), 4067; https://doi.org/10.3390/s19194067 - 20 Sep 2019
Cited by 43 | Viewed by 7882
Abstract
Unmanned Aerial Vehicles (UAVs) have recently been used in a wide variety of applications due to their versatility, reduced cost, rapid deployment, among other advantages. Search and Rescue (SAR) is one of the most prominent areas for the employment of UAVs in place [...] Read more.
Unmanned Aerial Vehicles (UAVs) have recently been used in a wide variety of applications due to their versatility, reduced cost, rapid deployment, among other advantages. Search and Rescue (SAR) is one of the most prominent areas for the employment of UAVs in place of a manned mission, especially because of its limitations on the costs, human resources, and mental and perception of the human operators. In this work, a real-time path-planning solution using multiple cooperative UAVs for SAR missions is proposed. The technique of Particle Swarm Optimization is used to solve a Model Predictive Control (MPC) problem that aims to perform search in a given area of interest, following the directive of international standards of SAR. A coordinated turn kinematic model for level flight in the presence of wind is included in the MPC. The solution is fully implemented to be embedded in the UAV on-board computer with DUNE, an on-board navigation software. The performance is evaluated using Ardupilot’s Software-In-The-Loop with JSBSim flight dynamics model simulations. Results show that, when employing three UAVs, the group reaches 50% Probability of Success 2.35 times faster than when a single UAV is employed. Full article
(This article belongs to the Special Issue Deployment and Navigation of Aerial Drones for Surveillance and Monitoring)
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22 pages, 4756 KiB  
Article
GNSS/LiDAR-Based Navigation of an Aerial Robot in Sparse Forests
by Antonio C. B. Chiella, Henrique N. Machado, Bruno O. S. Teixeira and Guilherme A. S. Pereira
Sensors 2019, 19(19), 4061; https://doi.org/10.3390/s19194061 - 20 Sep 2019
Cited by 23 | Viewed by 4157
Abstract
Autonomous navigation of unmanned vehicles in forests is a challenging task. In such environments, due to the canopies of the trees, information from Global Navigation Satellite Systems (GNSS) can be degraded or even unavailable. Also, because of the large number of obstacles, a [...] Read more.
Autonomous navigation of unmanned vehicles in forests is a challenging task. In such environments, due to the canopies of the trees, information from Global Navigation Satellite Systems (GNSS) can be degraded or even unavailable. Also, because of the large number of obstacles, a previous detailed map of the environment is not practical. In this paper, we solve the complete navigation problem of an aerial robot in a sparse forest, where there is enough space for the flight and the GNSS signals can be sporadically detected. For localization, we propose a state estimator that merges information from GNSS, Attitude and Heading Reference Systems (AHRS), and odometry based on Light Detection and Ranging (LiDAR) sensors. In our LiDAR-based odometry solution, the trunks of the trees are used in a feature-based scan matching algorithm to estimate the relative movement of the vehicle. Our method employs a robust adaptive fusion algorithm based on the unscented Kalman filter. For motion control, we adopt a strategy that integrates a vector field, used to impose the main direction of the movement for the robot, with an optimal probabilistic planner, which is responsible for obstacle avoidance. Experiments with a quadrotor equipped with a planar LiDAR in an actual forest environment is used to illustrate the effectiveness of our approach. Full article
(This article belongs to the Special Issue Deployment and Navigation of Aerial Drones for Surveillance and Monitoring)
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18 pages, 8904 KiB  
Article
On-Site 4-in-1 Alignment: Visualization and Interactive CAD Model Retrofitting Using UAV, LiDAR’s Point Cloud Data, and Video
by Pavan Kumar B. N., Ashok Kumar Patil, Chethana B. and Young Ho Chai
Sensors 2019, 19(18), 3908; https://doi.org/10.3390/s19183908 - 10 Sep 2019
Cited by 10 | Viewed by 3974
Abstract
Acquisition of 3D point cloud data (PCD) using a laser scanner and aligning it with a video frame is a new approach that is efficient for retrofitting comprehensive objects in heavy pipeline industrial facilities. This work contributes a generic framework for interactive retrofitting [...] Read more.
Acquisition of 3D point cloud data (PCD) using a laser scanner and aligning it with a video frame is a new approach that is efficient for retrofitting comprehensive objects in heavy pipeline industrial facilities. This work contributes a generic framework for interactive retrofitting in a virtual environment and an unmanned aerial vehicle (UAV)-based sensory setup design to acquire PCD. The framework adopts a 4-in-1 alignment using a point cloud registration algorithm for a pre-processed PCD alignment with the partial PCD, and frame-by-frame registration method for video alignment. This work also proposes a virtual interactive retrofitting framework that uses pre-defined 3D computer-aided design models (CAD) with a customized graphical user interface (GUI) and visualization of a 4-in-1 aligned video scene from a UAV camera in a desktop environment. Trials were carried out using the proposed framework in a real environment at a water treatment facility. A qualitative and quantitative study was conducted to evaluate the performance of the proposed generic framework from participants by adopting the appropriate questionnaire and retrofitting task-oriented experiment. Overall, it was found that the proposed framework could be a solution for interactive 3D CAD model retrofitting on a combination of UAV sensory setup-acquired PCD and real-time video from the camera in heavy industrial facilities. Full article
(This article belongs to the Special Issue Deployment and Navigation of Aerial Drones for Surveillance and Monitoring)
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20 pages, 6810 KiB  
Article
On the Use of the AIRA-UAS Corpus to Evaluate Audio Processing Algorithms in Unmanned Aerial Systems
by Caleb Rascon, Oscar Ruiz-Espitia and Jose Martinez-Carranza
Sensors 2019, 19(18), 3902; https://doi.org/10.3390/s19183902 - 10 Sep 2019
Cited by 6 | Viewed by 3262
Abstract
Audio analysis over an Unmanned Aerial Systems (UAS) is of interest it is an essential step for on-board sound source localization and separation. This could be useful for search & rescue operations, as well as for detection of unauthorized drone operations. In this [...] Read more.
Audio analysis over an Unmanned Aerial Systems (UAS) is of interest it is an essential step for on-board sound source localization and separation. This could be useful for search & rescue operations, as well as for detection of unauthorized drone operations. In this paper, an analysis of the previously introduced Acoustic Interactions for Robot Audition (AIRA)-UAS corpus is presented, which is a set of recordings produced by the ego-noise of a drone performing different aerial maneuvers and by other drones flying nearby. It was found that the recordings have a very low Signal-to-Noise Ratio (SNR), that the noise is dynamic depending of the drone’s movements, and that their noise signatures are highly correlated. Three popular filtering techniques were evaluated in this work in terms of noise reduction and signature extraction, which are: Berouti’s Non-Linear Noise Subtraction, Adaptive Quantile Based Noise Estimation, and Improved Minima Controlled Recursive Averaging. Although there was moderate success in noise reduction, no filter was able to keep intact the signature of the drone flying in parallel. These results are evidence of the challenge in audio processing over drones, implying that this is a field prime for further research. Full article
(This article belongs to the Special Issue Deployment and Navigation of Aerial Drones for Surveillance and Monitoring)
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20 pages, 10428 KiB  
Article
Motion Compensation for Radar Terrain Imaging Based on INS/GPS System
by Michal Labowski, Piotr Kaniewski and Piotr Serafin
Sensors 2019, 19(18), 3895; https://doi.org/10.3390/s19183895 - 10 Sep 2019
Cited by 5 | Viewed by 2611
Abstract
In order to obtain good quality radar terrain images using an aerial-based synthetic aperture radar, a motion compensation procedure must be applied. This procedure can use a precise navigation system in order to determine the aircraft’s position and velocity. A major challenge is [...] Read more.
In order to obtain good quality radar terrain images using an aerial-based synthetic aperture radar, a motion compensation procedure must be applied. This procedure can use a precise navigation system in order to determine the aircraft’s position and velocity. A major challenge is to design a motion compensation procedure that can operate in real time, which is crucial to ensure convenient data for a human analyst. The article discusses a possibility of Inertial Measurement System (INS)/Global Positioning System (GPS) navigation system usage in such a radar imaging system. A Kalman filter algorithm designed for this system is described herein, and its modifications introduced by the authors allow the use of navigational data not aligned in time and captured with different frequencies. The presented navigation system was tested using measured data. Radar images obtained with the INS/GPS-based motion compensation system were compared to the INS-only results and images obtained without navigation corrections. The evaluation results presented in the paper show that the INS/GPS system allows for better reduction of geometric distortions in images compared to the INS-based approach, which makes it more suitable for typical applications. Full article
(This article belongs to the Special Issue Deployment and Navigation of Aerial Drones for Surveillance and Monitoring)
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16 pages, 2053 KiB  
Article
Cooperative Unmanned Aerial System Reconnaissance in a Complex Urban Environment and Uneven Terrain
by Petr Stodola, Jan Drozd, Jan Mazal, Jan Hodický and Dalibor Procházka
Sensors 2019, 19(17), 3754; https://doi.org/10.3390/s19173754 - 30 Aug 2019
Cited by 19 | Viewed by 3432
Abstract
Using unmanned robotic systems in military operations such as reconnaissance or surveillance, as well as in many civil applications, is common practice. In this article, the problem of monitoring the specified area of interest by a fleet of unmanned aerial systems is examined. [...] Read more.
Using unmanned robotic systems in military operations such as reconnaissance or surveillance, as well as in many civil applications, is common practice. In this article, the problem of monitoring the specified area of interest by a fleet of unmanned aerial systems is examined. The monitoring is planned via the Cooperative Aerial Model, which deploys a number of waypoints in the area; these waypoints are visited successively by unmanned systems. The original model proposed in the past assumed that the area to be explored is perfectly flat. A new formulation of this model is introduced in this article so that the model can be used in a complex environment with uneven terrain and/or with many obstacles, which may occlude some parts of the area of interest. The optimization algorithm based on the simulated annealing principles is proposed for positioning of waypoints to cover as large an area as possible. A set of scenarios has been designed to verify and evaluate the proposed approach. The key experiments are aimed at finding the minimum number of waypoints needed to explore at least the minimum requested portion of the area. Furthermore, the results are compared to the algorithm based on the lawnmower pattern. Full article
(This article belongs to the Special Issue Deployment and Navigation of Aerial Drones for Surveillance and Monitoring)
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18 pages, 3766 KiB  
Article
Trajectory Optimization in a Cooperative Aerial Reconnaissance Model
by Petr Stodola, Jan Drozd, Jan Nohel, Jan Hodický and Dalibor Procházka
Sensors 2019, 19(12), 2823; https://doi.org/10.3390/s19122823 - 24 Jun 2019
Cited by 13 | Viewed by 3133
Abstract
In recent years, the use of modern technology in military operations has become standard practice. Unmanned systems play an important role in operations such as reconnaissance and surveillance. This article examines a model for planning aerial reconnaissance using a fleet of mutually cooperating [...] Read more.
In recent years, the use of modern technology in military operations has become standard practice. Unmanned systems play an important role in operations such as reconnaissance and surveillance. This article examines a model for planning aerial reconnaissance using a fleet of mutually cooperating unmanned aerial vehicles to increase the effectiveness of the task. The model deploys a number of waypoints such that, when every waypoint is visited by any vehicle in the fleet, the area of interest is fully explored. The deployment of waypoints must meet the conditions arising from the technical parameters of the sensory systems used and tactical requirements of the task at hand. This paper proposes an improvement of the model by optimizing the number and position of waypoints deployed in the area of interest, the effect of which is to improve the trajectories of individual unmanned systems, and thus increase the efficiency of the operation. To achieve this optimization, a modified simulated annealing algorithm is proposed. The improvement of the model is verified by several experiments. Two sets of benchmark problems were designed: (a) benchmark problems for verifying the proposed algorithm for optimizing waypoints, and (b) benchmark problems based on typical reconnaissance scenarios in the real environment to prove the increased effectiveness of the reconnaissance operation. Moreover, an experiment in the SteelBeast simulation system was also conducted. Full article
(This article belongs to the Special Issue Deployment and Navigation of Aerial Drones for Surveillance and Monitoring)
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19 pages, 33932 KiB  
Article
Achieving Tiered Model Quality in 3D Structure from Motion Models Using a Multi-Scale View-Planning Algorithm for Automated Targeted Inspection
by Trent J. Okeson, Benjamin J. Barrett, Samuel Arce, Cory A. Vernon, Kevin W. Franke and John D. Hedengren
Sensors 2019, 19(12), 2703; https://doi.org/10.3390/s19122703 - 16 Jun 2019
Cited by 8 | Viewed by 4492
Abstract
This study presents a novel multi-scale view-planning algorithm for automated targeted inspection using unmanned aircraft systems (UAS). In industrial inspection, it is important to collect the most relevant data to keep processing demands, both human and computational, to a minimum. This study investigates [...] Read more.
This study presents a novel multi-scale view-planning algorithm for automated targeted inspection using unmanned aircraft systems (UAS). In industrial inspection, it is important to collect the most relevant data to keep processing demands, both human and computational, to a minimum. This study investigates the viability of automated targeted multi-scale image acquisition for Structure from Motion (SfM)-based infrastructure modeling. A traditional view-planning approach for SfM is extended to a multi-scale approach, planning for targeted regions of high, medium, and low priority. The unmanned aerial vehicle (UAV) can traverse the entire aerial space and facilitates collection of an optimized set of views, both close to and far away from areas of interest. The test case for field validation is the Tibble Fork Dam in Utah. Using the targeted multi-scale flight planning, a UAV automatically flies a tiered inspection using less than 25% of the number of photos needed to model the entire dam at high-priority level. This results in approximately 75% reduced flight time and model processing load, while still maintaining high model accuracy where needed. Models display stepped improvement in visual clarity and SfM reconstruction integrity by priority level, with the higher priority regions more accurately modeling smaller and finer features. A resolution map of the final tiered model is included. While this study focuses on multi-scale view planning for optical sensors, the methods potentially extend to other remote sensors, such as aerial LiDAR. Full article
(This article belongs to the Special Issue Deployment and Navigation of Aerial Drones for Surveillance and Monitoring)
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11 pages, 706 KiB  
Article
Asymptotically Optimal Deployment of Drones for Surveillance and Monitoring
by Andrey V. Savkin and Hailong Huang
Sensors 2019, 19(9), 2068; https://doi.org/10.3390/s19092068 - 03 May 2019
Cited by 40 | Viewed by 5310
Abstract
This paper studies the problem of placing a set of drones for surveillance of a ground region. The main goal is to determine the minimum number of drones necessary to be deployed at a given altitude to monitor the region. An easily implementable [...] Read more.
This paper studies the problem of placing a set of drones for surveillance of a ground region. The main goal is to determine the minimum number of drones necessary to be deployed at a given altitude to monitor the region. An easily implementable algorithm to estimate the minimum number of drones and determine their locations is developed. Moreover, it is proved that this algorithm is asymptotically optimal in the sense that the ratio of the number of drones required by this algorithm and the minimum number of drones converges to one as the area of the ground region tends to infinity. The proof is based on Kershner’s theorem from combinatorial geometry. Illustrative examples and comparisons with other existing methods show the efficiency of the developed algorithm. Full article
(This article belongs to the Special Issue Deployment and Navigation of Aerial Drones for Surveillance and Monitoring)
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11 pages, 658 KiB  
Article
Proactive Deployment of Aerial Drones for Coverage over Very Uneven Terrains: A Version of the 3D Art Gallery Problem
by Andrey V. Savkin and Hailong Huang
Sensors 2019, 19(6), 1438; https://doi.org/10.3390/s19061438 - 23 Mar 2019
Cited by 42 | Viewed by 4565
Abstract
The paper focuses on surveillance and monitoring using aerial drones. The aim is to estimate the minimal number of drones necessary to monitor a given area of a very uneven terrain. The proposed problem may be viewed as a drone version of the [...] Read more.
The paper focuses on surveillance and monitoring using aerial drones. The aim is to estimate the minimal number of drones necessary to monitor a given area of a very uneven terrain. The proposed problem may be viewed as a drone version of the 3D Art Gallery Problem. A computationally simple algorithm to calculate an upper estimate of the minimal number of drones together with their locations is developed. Computer simulations are conducted to demonstrate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Deployment and Navigation of Aerial Drones for Surveillance and Monitoring)
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