Biologically Inspired Complete Coverage Path Planning Algorithm Based on Q-Learning

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 10; p. 4647
• Main Authors: Tan, Xiangquan, Han, Linhui, Gong, Hao, Wu, Qingwen
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 11.05.2023; MDPI
• Subjects: Algorithms; biologically inspired neural network; complete coverage path planning; Deep learning; Efficiency; Hierarchies; mobile robot; Neural networks; Neurons; Planning; Q-learning; Robots; mobile robot; Q-learning; biologically inspired neural network; complete coverage path planning
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23104647

Complete coverage path planning requires that the mobile robot traverse all reachable positions in the environmental map. Aiming at the problems of local optimal path and high path coverage ratio in the complete coverage path planning of the traditional biologically inspired neural network algorithm, a complete coverage path planning algorithm based on Q-learning is proposed. The global environment information is introduced by the reinforcement learning method in the proposed algorithm. In addition, the Q-learning method is used for path planning at the positions where the accessible path points are changed, which optimizes the path planning strategy of the original algorithm near these obstacles. Simulation results show that the algorithm can automatically generate an orderly path in the environmental map, and achieve 100% coverage with a lower path repetition ratio.