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作 者:Qi ZHANG Zongwu XIE Baoshi CAO Yang LIU
机构地区:[1]State Key Laboratory of Robotics and System,Harbin Institute of Technology,Harbin 150001,China
出 处:《Chinese Journal of Aeronautics》2023年第3期436-448,共13页中国航空学报(英文版)
基 金:supported by the National Natural Science Foundation of China(No.91848202);the Special Foundation(Pre-Station)of China Postdoctoral Science(No.2021TQ0089)。
摘 要:Bolt assembly by robots is a vital and difficult task for replacing astronauts in extravehicular activities(EVA),but the trajectory efficiency still needs to be improved during the wrench insertion into hex hole of bolt.In this paper,a policy iteration method based on reinforcement learning(RL)is proposed,by which the problem of trajectory efficiency improvement is constructed as an issue of RL-based objective optimization.Firstly,the projection relation between raw data and state-action space is established,and then a policy iteration initialization method is designed based on the projection to provide the initialization policy for iteration.Policy iteration based on the protective policy is applied to continuously evaluating and optimizing the action-value function of all state-action pairs till the convergence is obtained.To verify the feasibility and effectiveness of the proposed method,a noncontact demonstration experiment with human supervision is performed.Experimental results show that the initialization policy and the generated policy can be obtained by the policy iteration method in a limited number of demonstrations.A comparison between the experiments with two different assembly tolerances shows that the convergent generated policy possesses higher trajectory efficiency than the conservative one.In addition,this method can ensure safety during the training process and improve utilization efficiency of demonstration data.
关 键 词:Bolt assembly Policy initialization Policy iteration Reinforcement learning(RL) Robotic assembly Trajectory efficiency
分 类 号:V46[航空宇航科学与技术—航空宇航制造工程]
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