TY - GEN
T1 - An Improved Potential Field-Based Probabilistic Roadmap Algorithm for Path Planning
AU - Zhang, Yonghao
AU - Zhang, Lijuan
AU - Lei, Lei
AU - Xu, Fengyou
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Probabilistic Roadmap (PRM) is one of the most important path planning techniques. It has been widely applied in mobile robot navigation for its simplicity. However, when there are narrow passages in the environment, the planning efficiency of PRM is greatly reduced. To solve this problem, this article proposes an improved potential field-based probabilistic roadmap algorithm. Making use of virtual potential field strategy, the targeting environment is represented with a quantifiable potential field map, so that the obstacle information is clearly expressed. Next, a partition-based sampling strategy is developed to improve the number of sampling points in dense obstacle areas while keeping sampling points uniformly distributed. Finally, some critical points are added with a new eight-directional detection method, thus the success rate of path planning is effectively improved. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.
AB - Probabilistic Roadmap (PRM) is one of the most important path planning techniques. It has been widely applied in mobile robot navigation for its simplicity. However, when there are narrow passages in the environment, the planning efficiency of PRM is greatly reduced. To solve this problem, this article proposes an improved potential field-based probabilistic roadmap algorithm. Making use of virtual potential field strategy, the targeting environment is represented with a quantifiable potential field map, so that the obstacle information is clearly expressed. Next, a partition-based sampling strategy is developed to improve the number of sampling points in dense obstacle areas while keeping sampling points uniformly distributed. Finally, some critical points are added with a new eight-directional detection method, thus the success rate of path planning is effectively improved. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.
KW - artificial potential field
KW - critical point
KW - path planning
KW - probabilistic roadmap
UR - https://www.scopus.com/pages/publications/85142456932
U2 - 10.1109/ICACR55854.2022.9935557
DO - 10.1109/ICACR55854.2022.9935557
M3 - Conference contribution
AN - SCOPUS:85142456932
T3 - 2022 6th International Conference on Automation, Control and Robots, ICACR 2022
SP - 195
EP - 199
BT - 2022 6th International Conference on Automation, Control and Robots, ICACR 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Automation, Control and Robots, ICACR 2022
Y2 - 23 September 2022 through 25 September 2022
ER -