Paper
28 August 2024 Research on multivessel collision avoidance decision based on improved artificial potential field algorithm
Song Shaoting, Jinshan Zhu, Zheng Peijie, Sun Xiaoxiao, Bin Mei
Author Affiliations +
Proceedings Volume 13251, Ninth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024); 132516D (2024) https://doi.org/10.1117/12.3039989
Event: 9th International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024), 2024, Guilin, China
Abstract
Currently, as a path planning method based on the concept of physical field, the artificial potential field method has attracted wide attention because of its intuition and easy implementation characteristics. However, the traditional artificial potential field method has problems such as unreaccessibility and local minimum, which limit its application in multi-ship obstacle avoidance. To solve these problems, this study proposes an improved artificial potential field method. In this paper, we adjust the repulsion function, add the relative distance between the ship and the target point, so that the ship can successfully reach the target point, and solve the problem of unable to reach the original target. For the local minimum problem, this paper combines the artificial potential field method with the improved particle swarm algorithm to enhance the global search ability and improve the efficiency and robustness of path planning. In the simulation experiment under the dynamic environment, the improvement method shows significant advantages, which can effectively avoid the collision and quickly adapt to the change of obstacles, and generate a safe obstacle avoidance path.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Song Shaoting, Jinshan Zhu, Zheng Peijie, Sun Xiaoxiao, and Bin Mei "Research on multivessel collision avoidance decision based on improved artificial potential field algorithm", Proc. SPIE 13251, Ninth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024), 132516D (28 August 2024); https://doi.org/10.1117/12.3039989
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KEYWORDS
Detection and tracking algorithms

Collision avoidance

Particles

Particle swarm optimization

Computer simulations

Control systems

Genetic algorithms

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