A Coverage Path Planning Method with Reinforcement Learning Considering Manufacturing Process Uncertainty

LI Yanzheng; LIU Yinhua; ZHAO Wenzheng; SUN Rui

doi:10.13433/j.cnki.1003-8728.20220203

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A Coverage Path Planning Method with Reinforcement Learning Considering Manufacturing Process Uncertainty

更新时间：2026-07-10

- A Coverage Path Planning Method with Reinforcement Learning Considering Manufacturing Process Uncertainty
- Mechanical Science and Technology for Aerospace Engineering Vol. 43, Issue 1, Pages: 9-15(2024)
- 作者机构：
  
  1. 上海理工大学机械工程学院,上海,200093
  2. 上海交通大学机械与动力工程学院,上海,200240
- 作者简介：
- 基金信息：
- DOI：10.13433/j.cnki.1003-8728.20220203
  CLC：
- Online First：25 January 2024，
  
  Published：2024
- 稿件说明：
移动端阅览
LI Yanzheng, LIU Yinhua, ZHAO Wenzheng, et al. A Coverage Path Planning Method with Reinforcement Learning Considering Manufacturing Process Uncertainty[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(1): 9-15.
DOI：

LI Yanzheng, LIU Yinhua, ZHAO Wenzheng, et al. A Coverage Path Planning Method with Reinforcement Learning Considering Manufacturing Process Uncertainty[J]. Mechanical Science and Technology for Aerospace Engineering, 2024, 43(1): 9-15. DOI： 10.13433/j.cnki.1003-8728.20220203.

摘要

机器人扫描测量系统在汽车质量检测领域获得广泛应用，尤其数模环境下基于仿生优化算法的视点采样与路径规划研究取得较大进展。然而，基于名义数模环境的路径规划结果难以适用实际不确定性检测环境。为此，本文提出基于改进的蒙特卡洛树搜索的视点自适应采样方法，在线生成工业机器人运动轨迹。通过车门内板案例的对比分析，验证本文方法的有效性，为实现不确定制造环境下工艺路径的在线规划提供理论依据。

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.

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Related Author

WU Zhipeng

ZHAO Anan

ZHENG Wei

TIAN Wei

CUI Haihua

Related Institution

AVIC Xi'an Aircraft Industry(Group)Company LTD.

College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics

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