Research on Path Planning Strategy of Rescue Robot Based on Reinforcement Learning,ERICDATA高等教育知識庫
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篇名
Research on Path Planning Strategy of Rescue Robot Based on Reinforcement Learning
並列篇名
Research on Path Planning Strategy of Rescue Robot Based on Reinforcement Learning
作者 Ying-Ming ShiZhiyuan Zhang
英文摘要

How rescue robots reach their destinations quickly and efficiently has become a hot research topic in recent years. Aiming at the complex unstructured environment faced by rescue robots, this paper proposes an artificial potential field algorithm based on reinforcement learning. Firstly, use the traditional artificial potential field method to perform basic path planning for the robot. Secondly, in order to solve the local minimum problem in planning and improve the robot’s adaptive ability, the reinforcement learning algorithm is run by fixing preset parameters on the simulation platform. After intensive training, the robot continuously improves the decision-making ability of crossing typical concave obstacles. Finally, through simulation experiments, it is concluded that the rescue robot can combine the artificial potential field method and reinforcement learning to improve the ability to adapt to the environment, and can reach the destination with the optimal route.

 

起訖頁 187-194
關鍵詞 rescue robotpotential field algorithmreinforcement learningoptimal route
刊名 電腦學刊  
期數 202206 (33:3期)
DOI 10.53106/199115992022063303015  複製DOI
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