TY - GEN
T1 - APF-RRT∗
T2 - 9th International Conference on Mechatronics and Robotics Engineering, ICMRE 2023
AU - Ma, Benshan
AU - Wei, Chao
AU - Huang, Qing
AU - Hu, Jibin
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Path planning is a decisive module of mobile robots and its time efficiency significantly affects the safety of the robots. Sampling-based methods have achieved great success in the robotic path planning domain. However, poor time efficiency is still a serious limitation when they are applied to a crowded environment. In this paper, we combine the RRT∗ algorithm and artificial potential field(APF) technic and propose an efficient sampling-based path planning method named APF-RRT*. Utilizing the prior knowledge of the mission and the environment, we construct APFs for the start point, the goal point, the reference path, and the obstacles. Then we modify the random sampling step of the RRT∗ algorithm. With the guidance of APF, the random sample points are closer to the optimal path, and useless sample points greatly decrease. Results show that the proposed APF-RRT∗ outperforms state-of-the-art sampling-based methods in convergence rate, sampling effectiveness, and time efficiency.
AB - Path planning is a decisive module of mobile robots and its time efficiency significantly affects the safety of the robots. Sampling-based methods have achieved great success in the robotic path planning domain. However, poor time efficiency is still a serious limitation when they are applied to a crowded environment. In this paper, we combine the RRT∗ algorithm and artificial potential field(APF) technic and propose an efficient sampling-based path planning method named APF-RRT*. Utilizing the prior knowledge of the mission and the environment, we construct APFs for the start point, the goal point, the reference path, and the obstacles. Then we modify the random sampling step of the RRT∗ algorithm. With the guidance of APF, the random sample points are closer to the optimal path, and useless sample points greatly decrease. Results show that the proposed APF-RRT∗ outperforms state-of-the-art sampling-based methods in convergence rate, sampling effectiveness, and time efficiency.
KW - RRT
KW - artificial potential field
KW - path planning
KW - sampling-based algorithm
UR - http://www.scopus.com/inward/record.url?scp=85159072873&partnerID=8YFLogxK
U2 - 10.1109/ICMRE56789.2023.10106516
DO - 10.1109/ICMRE56789.2023.10106516
M3 - Conference contribution
AN - SCOPUS:85159072873
T3 - 2023 9th International Conference on Mechatronics and Robotics Engineering, ICMRE 2023
SP - 207
EP - 213
BT - 2023 9th International Conference on Mechatronics and Robotics Engineering, ICMRE 2023
A2 - Ma, Yongsheng
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 February 2023 through 12 February 2023
ER -