Fixed-Wing UAV Formation Control Design With Collision Avoidance Based on an Improved Artificial Potential Field

This paper proposes a formation generation algorithm and formation obstacle avoidance strategy for multiple unmanned surface vehicles (USVs). The proposed formation generation algorithm implements an approach combining a virtual structure and artificial potential field (VSAPF), which provides a high accuracy of formation shape keeping and flexibility of formation shape change. To solve the obstacle avoidance problem of the multi-USV system, an improved dynamic window approach is applied to the formation reference point, which considers the movement ability of the USV. By applying this method, the USV formation can avoid obstacles while maintaining its shape. The combination of the virtual structure and artificial potential field has the advantage of less calculations, so that it can ensure the real-time performance of the algorithm and convenience for deployment on an actual USV. Various simulation results for a group of USVs are provided to demonstrate the effectiveness of the proposed algorithms.

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Dynamic artificial potential field based multi-robot formation control

2010 IEEE Instrumentation & Measurement Technology Conference Proceedings ◽

10.1109/imtc.2010.5488238 ◽

2010 ◽

Cited By ~ 21

Author(s):

Min Zhang ◽

Yi Shen ◽

Qiang Wang ◽

Yibo Wang

Keyword(s):

Potential Field ◽

Formation Control ◽

Artificial Potential Field ◽

Multi Robot

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Ship Domain Model for Multi-ship Collision Avoidance Decision-making with COLREGs Based on Artificial Potential Field

TransNav the International Journal on Marine Navigation and Safety of Sea Transportation ◽

10.12716/1001.11.01.09 ◽

2017 ◽

Vol 11 (1) ◽

pp. 85-92 ◽

Cited By ~ 4

Author(s):

TengFei Wang ◽

Xin Ping Yan ◽

Yang Wang ◽

Qing Wu

Keyword(s):

Decision Making ◽

Collision Avoidance ◽

Potential Field ◽

Domain Model ◽

Artificial Potential Field ◽

Ship Collision

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Multi-agent Distributed Formation Control Based on Improved Artificial Potential Field and Neural Network for Connectivity Preservation

10.1109/icus52573.2021.9641295 ◽

2021 ◽

Author(s):

Bao Chen ◽

Hongjun Ma ◽

Haobo Kang ◽

Xinkai Liang

Keyword(s):

Neural Network ◽

Potential Field ◽

Formation Control ◽

Artificial Potential Field ◽

Connectivity Preservation ◽

Multi Agent

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Optimized artificial potential field algorithm to multi-unmanned aerial vehicle coordinated trajectory planning and collision avoidance in three-dimensional environment

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410019844434 ◽

2019 ◽

Vol 233 (16) ◽

pp. 6032-6043 ◽

Cited By ~ 2

Author(s):

Jun Tang ◽

Jiayi Sun ◽

Cong Lu ◽

Songyang Lao

Keyword(s):

Unmanned Aerial Vehicles ◽

Collision Avoidance ◽

Unmanned Aerial Vehicle ◽

Trajectory Planning ◽

Potential Field ◽

Three Dimensional ◽

Artificial Potential Field ◽

Aerial Vehicles ◽

Aerial Vehicle ◽

Vehicle Trajectory

Multi-unmanned aerial vehicle trajectory planning is one of the most complex global optimum problems in multi-unmanned aerial vehicle coordinated control. Results of recent research works on trajectory planning reveal persisting theoretical and practical problems. To mitigate them, this paper proposes a novel optimized artificial potential field algorithm for multi-unmanned aerial vehicle operations in a three-dimensional dynamic space. For all purposes, this study considers the unmanned aerial vehicles and obstacles as spheres and cylinders with negative electricity, respectively, while the targets are considered spheres with positive electricity. However, the conventional artificial potential field algorithm is restricted to a single unmanned aerial vehicle trajectory planning in two-dimensional space and usually fails to ensure collision avoidance. To deal with this challenge, we propose a method with a distance factor and jump strategy to resolve common problems such as unreachable targets and ensure that the unmanned aerial vehicle does not collide into the obstacles. The method takes companion unmanned aerial vehicles as the dynamic obstacles to realize collaborative trajectory planning. Besides, the method solves jitter problems using the dynamic step adjustment method and climb strategy. It is validated in quantitative test simulation models and reasonable results are generated for a three-dimensional simulated urban environment.

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UAV Group Formation Collision Avoidance Method Based on Second-Order Consensus Algorithm and Improved Artificial Potential Field

Symmetry ◽

10.3390/sym11091162 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1162 ◽

Cited By ~ 2

Author(s):

Yang Huang ◽

Jun Tang ◽

Songyang Lao

Keyword(s):

Collision Avoidance ◽

Potential Field ◽

Group Formation ◽

Target Position ◽

Field Method ◽

Second Order ◽

Consensus Algorithm ◽

Artificial Potential Field ◽

Potential Field Method ◽

Artificial Potential Field Method

The problem of collision avoidance of an unmanned aerial vehicle (UAV) group is studied in this paper. A collision avoidance method of UAV group formation based on second-order consensus algorithm and improved artificial potential field is proposed. Based on the method, the UAV group can form a predetermined formation from any initial state and fly to the target position in normal flight, and can avoid collision according to the improved smooth artificial potential field method when encountering an obstacle. The UAV group adopts the “leader–follower” strategy, that is, the leader UAV is the controller and flies independently according to the mission requirements, while the follower UAV follows the leader UAV based on the second-order consensus algorithm and formations gradually form during the flight. Based on the second-order consensus algorithm, the UAV group can achieve formation maintenance easily and the Laplacian matrix used in the algorithm is symmetric for an undirected graph. In the process of obstacle avoidance, the improved artificial potential field method can solve the jitter problem that the traditional artificial potential field method causes for the UAV and avoids violent jitter. Finally, simulation experiments of two scenarios were designed to verify the collision avoidance effect and formation retention effect of static obstacles and dynamic obstacles while the two UAV groups fly in opposite symmetry in the dynamic obstacle scenario. The experimental results demonstrate the effectiveness of the proposed method.

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Research on active collision avoidance algorithm for intelligent vehicle based on improved artificial potential field model

International Journal of Advanced Robotic Systems ◽

10.1177/1729881420911232 ◽

2020 ◽

Vol 17 (3) ◽

pp. 172988142091123

Author(s):

Chaochun Yuan ◽

Shuofeng Weng ◽

Jie Shen ◽

Long Chen ◽

Youguo He ◽

...

Keyword(s):

Collision Avoidance ◽

Potential Field ◽

Field Method ◽

Superior Performance ◽

Intelligent Vehicle ◽

Artificial Potential Field ◽

Sideslip Angle ◽

Lane Changing ◽

Model Based ◽

Braking Process

In this article, an active collision avoidance based on improved artificial potential field is proposed to satisfy collision avoidance for intelligent vehicle. A longitudinal safety distance model based on analysis of braking process and a lane-changing safety spacing model based on minimum time of lane changing under the constraint of sideslip angle are presented. In addition, an improved artificial potential field method is introduced, which represents the influence of environmental information with artificial force. Simulation results demonstrate the superior performance of the proposed algorithm over collision avoidance for intelligent vehicle.

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