j.issn.1000-0682.2021.01.017]
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基于深度强化学习的轨道式巡检机器人巡检驻点规划方法

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

Title:: The planning method of orbiting inspection robot’s inspection station based on deep reinforcement learning

作者:: 谷湘煜1; 彭志远1; 鲜开义1; 杨利萍1; 陈伟1; 邹娟1; 刘满禄2; 1．深圳市朗驰欣创科技股份有限公司，广州深圳 518000；
2.西南科技大学，四川绵阳 621000

Author(s):: GU Xiangyu; Peng Zhiyuan; XIAN Kaiyi; YANG Liping; CHEN Wei; ZOU Juan; LIU Manlu; 1.Shenzhen Langchi Xinchuang Technology Co., LTD.Guangzhou Shenzhen 518000, China;
2. Southwest University of Science and Technology, Sichuan Mianyang 621000, China

Keywords:: orbital inspection robot; stagnation planning; reinforcement learning

摘要:: 针对三峡水电站中轨道式巡检机器人的实际巡检需求，对巡检驻点规划展开研究。机器人的巡检驻点一般为人为根据经验及场景预设，因此难以规划最优驻点，影响检测效果。该文通过机器人本体搭载的各种传感设备信息构建综合环境感知函数，作为无模型强化学习算法的环境信息，引导机器人找到最优检测驻点。最后设计实验评估了该方法的有效性。

Abstract:: Aiming at the actual inspection requirements of the track-type inspection robots in the Three Gorges Hydropower Station, the patrol stagnation point planning was studied. The patrol station of the robot is generally artificially preset according to experience and scene, so it is difficult to plan the optimal stagnation point and affect the detection effect. In this paper, the comprehensive environment perception function is built by the various sensor equipments carried by the robot body and used as the environment information of the model-free reinforcement learning algorithm to guide the robot to find the optimal detection stagnation point. The final design experiment evaluated the effectiveness of the method.

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更新日期/Last Update: 1900-01-01