飞控与探测2025，Vol.8Issue(5)：11-24,14.DOI:10.20249/j.cnki.2096-5974.2025.05.002
空间机器人强泛化黏附爬行策略生成方法
Strong Generalization Adhesive Climbing Strategy Generation Method for Space Robot
摘要
Abstract
The space adhesive climbing robot can be attached to the outer surface of the spacecraft and complete the external inspection and operation tasks independently,which is an important way to realize the long-term unmanned in-orbit service of the spacecraft.In order to solve the problem of insufficient generalization ability of the control strategy of the adhesive climbing robot after un-expected changes in spacecraft surface characteristics,the mechanism of adhesion force is construc-ted under the framework of reinforcement learning,and the intensive reward function is construc-ted by combining the"follow-update"mechanism of the foot contact force,and the proximal policy optimization-clip(PPO-clip)algorithm is used to train and generate the adhesion crawling strategy of the robot in microgravity environment.The results show that the strategy convergence rate in-creases by about 14.81％under the"follow-update"mechanism of foot contact force.The climbing strategy obtained can maintain the adhesion stability of the robot on a flat surface,and has the ability to reach the target position with an arrival error of less than 0.1m.On surfaces with an unpredictable height change of±40mm and an unpredictable slope change of±18°,the climbing strategy obtained on the flat surface can achieve stable adhesion climbing of the robot.关键词
空间爬行机器人/微重力/足端黏附/奖励设计/强化学习Key words
space climbing robot/microgravity/foot adhesion/reward design/reinforcement learning分类
信息技术与安全科学引用本文复制引用
陈哲瑄,刘付成,孙俊,邸昕鹏,严余超,姚森纯..空间机器人强泛化黏附爬行策略生成方法[J].飞控与探测,2025,8(5):11-24,14.基金项目
国家自然科学基金(U20B2056,62204151,12102248) （U20B2056,62204151,12102248）
