TY - GEN
T1 - Multi-UUV Coordinated Path Planning with Collision Avoidance (CPP/CA) Based on Combination of Improved APF and A
AU - Wu, Xiaojun
AU - Long, Xu
AU - Yuan, Sheng
AU - Hu, Qiao
AU - Xie, Peiru
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Aiming at the path planning problem of cooperative underwater unmanned vehicles (UUVs) in the obstacle-constrained environment, this paper proposes a new obstacle-avoidance path planning algorithm. This algorithm combines artificial potential field (APF) method and A algorithm, which can realize multi-UUV coordinated path planning with collision avoidance (CPP/CA). Different from the current APF, this algorithm increases the repulsion between UUVs on the basis of optimizing the repulsive force of obstacles, sets temporary virtual target points, and uses the A algorithm to make UUVs pass through obstacles smoothly and move to the target point. This algorithm not only solves the issue of UUV falling into a local minimum, but also considers UUV companions to perform the same task, so that each UUV individually changes its path to avoid collisions with other UUVs and obstacles. Experimental simulation results show that the proposed algorithm has better obstacle avoidance performance and the ability to track targets, which can greatly improve the success rate of path planning.
AB - Aiming at the path planning problem of cooperative underwater unmanned vehicles (UUVs) in the obstacle-constrained environment, this paper proposes a new obstacle-avoidance path planning algorithm. This algorithm combines artificial potential field (APF) method and A algorithm, which can realize multi-UUV coordinated path planning with collision avoidance (CPP/CA). Different from the current APF, this algorithm increases the repulsion between UUVs on the basis of optimizing the repulsive force of obstacles, sets temporary virtual target points, and uses the A algorithm to make UUVs pass through obstacles smoothly and move to the target point. This algorithm not only solves the issue of UUV falling into a local minimum, but also considers UUV companions to perform the same task, so that each UUV individually changes its path to avoid collisions with other UUVs and obstacles. Experimental simulation results show that the proposed algorithm has better obstacle avoidance performance and the ability to track targets, which can greatly improve the success rate of path planning.
KW - A
KW - Artificial Potential Field
KW - Local Minimum
KW - Path Planning
KW - Underwater Unmanned Vehicle
UR - https://www.scopus.com/pages/publications/85132544780
U2 - 10.1109/ICCAR55106.2022.9782622
DO - 10.1109/ICCAR55106.2022.9782622
M3 - 会议稿件
AN - SCOPUS:85132544780
T3 - 2022 8th International Conference on Control, Automation and Robotics, ICCAR 2022
SP - 218
EP - 223
BT - 2022 8th International Conference on Control, Automation and Robotics, ICCAR 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Control, Automation and Robotics, ICCAR 2022
Y2 - 8 April 2022 through 10 April 2022
ER -