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Trajectory planning of free-floating space robot for non-cooperative tumbling target capture based on deep reinforcement learning

Published online by Cambridge University Press:  11 July 2025

Yaqiang Wei Open the ORCID record for Yaqiang Wei [Opens in a new window] ,
Xinlin Bai  and
Han Lu
Yaqiang Wei*
Affiliation:
State Key Laboratory of Mechanics and Control for Aerospace Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Xinlin Bai
Affiliation:
Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
Han Lu
Affiliation:
Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China University of Chinese Academy of Sciences, Beijing, China
*
Corresponding author: Yaqiang Wei; Email: weiyaqiang@nuaa.edu.cn
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Abstract

Capturing the non-cooperative tumbling target by the free-floating space robot stands as a crucial task within on-orbit servicing. However, the strong dynamic coupling of the base-spacecraft and the manipulator seriously disturbs the base-spacecraft, which reduces the power generation efficiency of solar panels and the communication quality with the earth station. In this paper, the trajectory planning method of the free-floating space robot for non-cooperative tumbling target capture based on deep reinforcement learning is proposed, which can reduce the disturbance of the base-spacecraft during target capture. First, the generalized Jacobian matrix of the space robot is derived, from which the dynamics model is obtained. The kinematics model of the space non-cooperative tumbling target is established. And the contact collision dynamics between the space robot and the tumbling target are analysed. Second, the twin delayed deep deterministic policy gradient algorithm is introduced to plan the trajectory for capturing the non-cooperative tumbling target, where apart from the motion parameters of the manipulator and the generalized manipulability of the space robot, the pose disturbance of the base-spacecraft is initially added to the reward function. Finally, the simulation for target capture is carried out. The results show that compared with the existing method, the proposed method converges faster with a larger reward, and the pose disturbance of the base-spacecraft is reduced. Moreover, the method performs well for capturing the non-cooperative tumbling target with different initial rotational angular velocities.

Keywords

free-floating space robotnon-cooperative tumbling target capturetrajectory planningdeep reinforcement learningpose disturbance of the base-spacecraft

Information

Type
Research Article
Information
Robotica , Volume 43 , Issue 7 , July 2025 , pp. 2674 - 2692
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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