机械科学与技术2024，Vol.43Issue(1)：9-15,7.DOI:10.13433/j.cnki.1003-8728.20220203
不确定检测环境下强化学习覆盖路径规划研究
A Coverage Path Planning Method with Reinforcement Learning Considering Manufacturing Process Uncertainty
摘要
Abstract
The robotic scanning system has been widely used in the quality inspection field of automobiles,especially the studies of viewpoint sampling and path planning based on the genetic optimization algorithm in the model-based environment.However,the path planning results based on the nominal models are difficult to apply to the actual inspection environment.To address this problem,a viewpoint adaptive sampling method is proposed based on an improved Monte Carlo tree search,and industrial robot motion trajectories are planned online.Finally,the case of the car door inner panel was used to illustrate the effectiveness of the method.关键词
光学检测/覆盖路径规划/制造误差/运动规划Key words
optical inspection/coverage path planning/manufacturing deviation/motion planning分类
交通工程引用本文复制引用
李彦征,刘银华,赵文政,孙芮..不确定检测环境下强化学习覆盖路径规划研究[J].机械科学与技术,2024,43(1):9-15,7.基金项目
国家自然科学基金项目(51875362)、上海市自然科学基金项目(21ZR1444500)及机械系统与振动国家重点实验开放基金项目(MSV202010) （51875362）
