TY - GEN
T1 - Path Planning based on Artificial Potential Field with Particle Swarm Optimization
AU - Cao, Li
AU - Xu, Ping
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - The artificial potential field method is commonly utilized in path planning, with its basic idea resembling an electromagnetic field. However, traditional artificial potential field methods suffer from issues such as unreachable targets and easily falling into local minima. To address these limitations, this paper proposes an enhanced artificial potential field method. It proposes an improved repulsive potential field function, which effectively resolves the problem of unreachable targets, and incorporates a road potential field, significantly enhancing the practicality of the algorithm. A fused particle swarm artificial potential field method is proposed, which solves the local optimum problem and improves the search efficiency. The proposed algorithm is subsequently validated using MATLAB, and the experimental results demonstrate that it effectively addresses the aforementioned challenges and plans a collision - free, safe path.
AB - The artificial potential field method is commonly utilized in path planning, with its basic idea resembling an electromagnetic field. However, traditional artificial potential field methods suffer from issues such as unreachable targets and easily falling into local minima. To address these limitations, this paper proposes an enhanced artificial potential field method. It proposes an improved repulsive potential field function, which effectively resolves the problem of unreachable targets, and incorporates a road potential field, significantly enhancing the practicality of the algorithm. A fused particle swarm artificial potential field method is proposed, which solves the local optimum problem and improves the search efficiency. The proposed algorithm is subsequently validated using MATLAB, and the experimental results demonstrate that it effectively addresses the aforementioned challenges and plans a collision - free, safe path.
KW - artificial potential field method
KW - particle swarm algorithm
KW - path planning
UR - https://www.scopus.com/pages/publications/105013959347
U2 - 10.1109/CCDC65474.2025.11090833
DO - 10.1109/CCDC65474.2025.11090833
M3 - 会议稿件
AN - SCOPUS:105013959347
T3 - Proceedings of the 37th Chinese Control and Decision Conference, CCDC 2025
SP - 4061
EP - 4066
BT - Proceedings of the 37th Chinese Control and Decision Conference, CCDC 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th Chinese Control and Decision Conference, CCDC 2025
Y2 - 16 May 2025 through 19 May 2025
ER -