TY - GEN
T1 - Research on Multi-Vehicle Trajectory Planning Based on Improved RRT-APF Method
AU - Zhang, Hao
AU - Feng, Fuyong
AU - Wei, Chao
AU - Zhao, Botong
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Multi-intelligent vehicle system can meet the needs of various missions. Efficient collaborative planning method is the key to improve its performance. In this paper, the trajectory planning of multi-vehicle system are studied. Firstly, the RRT algorithm which introduced pruning optimization and local replanning was used to plan the global path of each vehicle as the guide of trajectory planning. Then, deductive planning method and asymmetric potential field force are proposed to improve the artificial potential field method, and multi-vehicle sequence planning and collaborative planning are realized. This paper conducted simulation experiments to verify the effectiveness of the method. The experiments proved that the sequence planning method and collaborative planning method both can complete planning tasks under simple working conditions, and the collaborative planning method has higher efficiency under complex working conditions.
AB - Multi-intelligent vehicle system can meet the needs of various missions. Efficient collaborative planning method is the key to improve its performance. In this paper, the trajectory planning of multi-vehicle system are studied. Firstly, the RRT algorithm which introduced pruning optimization and local replanning was used to plan the global path of each vehicle as the guide of trajectory planning. Then, deductive planning method and asymmetric potential field force are proposed to improve the artificial potential field method, and multi-vehicle sequence planning and collaborative planning are realized. This paper conducted simulation experiments to verify the effectiveness of the method. The experiments proved that the sequence planning method and collaborative planning method both can complete planning tasks under simple working conditions, and the collaborative planning method has higher efficiency under complex working conditions.
KW - Artificial potential field method
KW - Asymmetric potential field force
KW - component
KW - Deductive planning
UR - http://www.scopus.com/inward/record.url?scp=105003948543&partnerID=8YFLogxK
U2 - 10.1109/ICEAAI64185.2025.10957333
DO - 10.1109/ICEAAI64185.2025.10957333
M3 - Conference contribution
AN - SCOPUS:105003948543
T3 - 2025 International Conference on Electrical Automation and Artificial Intelligence, ICEAAI 2025
SP - 1416
EP - 1420
BT - 2025 International Conference on Electrical Automation and Artificial Intelligence, ICEAAI 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 International Conference on Electrical Automation and Artificial Intelligence, ICEAAI 2025
Y2 - 10 January 2025 through 12 January 2025
ER -