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

doi:10.3390/s23104647

CIOMP OpenIR

	Biologically Inspired Complete Coverage Path Planning Algorithm Based on Q-Learning
	X. Tan, L. Han, H. Gong and Q. Wu
	2023
发表期刊	Sensors
ISSN	14248220
卷号	23 期号:10
摘要	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. © 2023 by the authors.
DOI	10.3390/s23104647
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收录类别	sci ; ei
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/67881
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	X. Tan, L. Han, H. Gong and Q. Wu. Biologically Inspired Complete Coverage Path Planning Algorithm Based on Q-Learning[J]. Sensors,2023,23(10).
APA	X. Tan, L. Han, H. Gong and Q. Wu.(2023).Biologically Inspired Complete Coverage Path Planning Algorithm Based on Q-Learning.Sensors,23(10).
MLA	X. Tan, L. Han, H. Gong and Q. Wu."Biologically Inspired Complete Coverage Path Planning Algorithm Based on Q-Learning".Sensors 23.10(2023).

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