A survey of formation control and motion planning of multiple unmanned vehicles

Liu, Yuanchang; Bucknall, Rwg

doi:10.1017/s0263574718000218

Cited by 139 publications

(91 citation statements)

References 101 publications

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“…Unmanned surface vehicle (USV), which has attracted significant research attention in recent years, is used for dangerous and inhospitable missions [1,2]. At present, a USV mainly consists of a motion control system [3], sensor system, and communication system.…”

Section: Introductionmentioning

confidence: 99%

Second Path Planning for Unmanned Surface Vehicle Considering the Constraint of Motion Performance

Fan

Liao

et al. 2019

JMSE

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When utilizing the traditional path planning method for unmanned surface vehicles (USVs), ‘planning-failure’ is a common phenomenon caused by the inflection points of large curvatures in the planned path, which exceed the performances of USVs. This paper presents a second path planning method (SPP), which is an initial planning path optimization method based on the geometric relationship of the three-point path. First, to describe the motion performance of a USV in conjunction with the limited test data, a method of integral nonlinear least squares identification is proposed to rapidly obtain the motion constraint of the USV merely by employing a zig zag test. It is different from maneuverability identification, which is performed in combination with various tests. Second, the curvature of the planned path is limited according to the motion performance of the USV based on the traditional path planning, and SPP is proposed to make the maximum curvature radius of the optimized path smaller than the rotation curvature radius of the USV. Finally, based on the ‘Dolphin 1’ prototype USV, comparative simulation experiments were carried out. In the experiment, the path directly obtained by the initial path planning and the path optimized by the SPP method were considered as the tracking target path. The artificial potential field method was used as an example for the initial path planning. The experimental results demonstrate that the tracking accuracy of the USV significantly improved after the path optimization using the SPP method.

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Section: Introductionmentioning

confidence: 99%

Second Path Planning for Unmanned Surface Vehicle Considering the Constraint of Motion Performance

Fan

Liao

et al. 2019

JMSE

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“…Besides their high popularity, standard quadrotors are challenged by their highly non-linear, strongly coupled and under-actuated dynamics that implies several limitations regarding both the executable maneuvers set and the efficiently achievable tasks. To overcome these drawbacks, the focus of aerial robotics community is currently moving toward cooperative approaches where the task execution is needed for a formation of UAV platforms, rather than a single improved vehicle [12][13][14][15]. According to the multi-agent paradigm, the idea is indeed to combine the limited single vehicle capabilities in order to efficiently solve complex tasks, as, for instance, multiple targets localization over a vast area [16], cumbersome payload transportation [17], and communication relay chain establishment [18], to cite a few.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Optimization of UAVs Formation Visual Tracking

et al. 2019

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The use of unmanned vehicles to perform tiring, hazardous, repetitive tasks, is becoming a reality out of the academy laboratories, getting more and more interest for several application fields from the industrial, to the civil, to the military contexts. In particular, these technologies appear quite promising when they employ several low-cost resource-constrained vehicles leveraging their coordination to perform complex tasks with efficiency, flexibility, and adaptation that are superior to those of a single agent (even if more instrumented). In this work, we study one of said applications, namely the visual tracking of an evader (target) by means of a fleet of autonomous aerial vehicles, with the specific aim of focusing on the target so as to perform an accurate position estimation while concurrently allowing a wide coverage over the monitored area so as to limit the probability of losing the target itself. These clearly conflicting objectives call for an optimization approach that is here developed: by considering both aforementioned aspects and the cooperative capabilities of the fleet, the designed algorithm allows controling in real time the single fields of view so as to counteract evasion maneuvers and maximize an overall performance index. The proposed strategy is discussed and finally assessed through the realistic Gazebo-ROS simulation framework.

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“…Next, the proposed attack involves designing sophisticated attack strategies to achieve the specific purpose, where the attacker disguises as an obstacle to fool the mobile robot into a preset trap (the trap could be a pothole, a cage or an area where the communication is invalid). Note that path distance and transition time are two commonly-used optimization objective in robot navigation (path planning) [17]. And it's a variational calculus problem to find the exact trajectory in an obstacle field, and the analytic solution can only be obtained for some simplest cases [18].…”

Section: Introductionmentioning

confidence: 99%

Learning-based Intelligent Attack against Formation Control with Obstacle-avoidance

Chen

et al. 2019

2019 American Control Conference (ACC)

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The security issue of mobile robots have attracted considerable attention in recent years. Most existing works focus on detection and countermeasures for some classic attacks from cyberspace. Nevertheless, those work are generally based on some prior assumptions for the attacker (e.g., the system dynamics is known, or internal access is compromised). A few work are delicated to physical attacks, however, there still lacks certain intelligence and advanced control design. In this paper, we propose a physical-based and intelligent attack framework against the obstacle-avoidance of mobile robots. The novelty of our work lies in the following: i) Without any prior information of the system dynamics, the attacker can learn the detection area and goal position of a mobile robot by trial and observation, and the obstacle-avoidance mechanism is learned by support vector regression (SVR) method; ii) Considering different attack requirements, different attack strategies are proposed to implement the attack efficiently; iii) The framework is suitable for holonomic and non-holonomic mobile robots, and the algorithm performance analysis about time complexity and optimality is provided. Furthermore, the condition is obtained to guarantee the success of the attack. Simulations illustrate the effectiveness of the proposed framework.The authors are with the

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A survey of formation control and motion planning of multiple unmanned vehicles

Cited by 139 publications

References 101 publications

Second Path Planning for Unmanned Surface Vehicle Considering the Constraint of Motion Performance

Second Path Planning for Unmanned Surface Vehicle Considering the Constraint of Motion Performance

Cooperative Optimization of UAVs Formation Visual Tracking

Learning-based Intelligent Attack against Formation Control with Obstacle-avoidance

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