Reliable Path Planning Algorithm Based on Improved Artificial Potential Field Method

• Published in: IEEE access Vol. 10; p. 1
• Main Authors: Luo, Jie, Wang, Zhong-Xun, Pan, Kang-Lu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Artificial potential field method; Autonomous vehicles; Collision avoidance; Heuristic algorithms; LSTM; Machine learning; Obstacle avoidance; Path planning; Potential fields; Reliability; Robot kinematics; Unmanned vehicles; Vehicle dynamics; Virtual potential field detection circle model
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3212741

In order to solve the "minimum trap" of artificial potential field method and the limitation of traditional path planning algorithm in dynamic obstacle environment, a path planning control algorithm based on improved artificial potential field method is proposed. Firstly, a virtual potential field detection circle model (VPFDCM) with adjustable radius is proposed to detect the "minimum trap" formed by the repulsion field of obstacles in advance. And the motion model of unmanned vehicle is established. Combined with the improved reinforcement learning algorithm based on Long Short-Term Memory(LSTM), the radius of virtual potential field detection circle is adjusted to achieve effective avoidance of dynamic obstacles, The reliable online collision free path planning of unmanned vehicle in semi closed dynamic obstacle environment is realized. Finally, the reliability and robustness of the algorithm are verified by MATLAB simulation. The simulation results show that the improved artificial potential field method can effectively solve the problem of unmanned vehicle falling into the "minimum trap" and improve the reliability of unmanned vehicle movement. Compared with the traditional artificial potential field method, the improved artificial potential field method can achieve more than 90% success rate in obstacle avoidance.