A Coverage Path Planning Method with Reinforcement Learning Considering Manufacturing Process Uncertainty
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.
国家科技期刊平台
NSTL
万方数据
维普
