Biologically Inspired Complete Coverage Path Planning Algorithm Based on Q-Learning
文献类型：期刊论文
| 作者 | X. Tan, L. Han, H. Gong and Q. Wu |
| 刊名 | Sensors
 |
| 出版日期 | 2023 |
| 卷号 | 23期号:10 |
| ISSN号 | 14248220 |
| DOI | 10.3390/s23104647 |
| 英文摘要 | 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. |
| URL标识 | 查看原文 |
| 源URL | [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). |
入库方式： OAI收割
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