Redundant-Motion Coordination and Base Disturbance Suppression of a 6R1P Free-Floating Space Manipulator Based on Deep Reinforcement Learning

Free-floating space manipulators are strongly coupled systems in which manipulator motion affects spacecraft base motion through momentum exchange, making simultaneous end-effector control and disturbance suppression challenging. This work investigates how an additional actuated prismatic degree of freedom influences whole-arm coordination in a 6R1P free-floating space manipulator. Compared with a fixed-length 6R configuration, the prismatic joint enlarges the feasible motion space and introduces an additional motion-allocation direction for full-pose tasks under generalized-Jacobian constraints. A proximal policy optimization (PPO)-based controller is developed for full-pose reaching with spacecraft-motion-aware objectives. Simulation results show that the 6R1P configuration improves reaching performance and reduces spacecraft reaction compared with the locked-prismatic 6R baseline. Trajectory-level dynamic reconstruction further reveals that the disturbance reduction is not caused by direct cancellation from the prismatic joint itself, but mainly by configuration-dependent redistribution of revolute-joint motions and enhanced mutual cancellation among their reaction contributions. These results demonstrate that telescopic redundancy provides a mechanism for coordinated motion allocation in free-floating manipulation, enabling learned policies to exploit additional degrees of freedom for improved task execution and reduced spacecraft disturbance.

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Publication Details

Journal
Machines
Published
2026-09-29
DOI
https://doi.org/10.3390/machines14101120
Primary Topic
Space Satellite Systems and Control
Type
article
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article

Redundant-Motion Coordination and Base Disturbance Suppression of a 6R1P Free-Floating Space Manipulator Based on Deep Reinforcement Learning

Jiaqi Duan, Zelin Yang, Tongtong Li, Yanbo Wang et al.
Machines
Space Satellite Systems and Control
article

Redundant-Motion Coordination and Base Disturbance Suppression of a 6R1P Free-Floating Space Manipulator Based on Deep Reinforcement Learning

Jiaqi Duan, Zelin Yang, Tongtong Li, Yanbo Wang, Shize Qin, Jian Zhao, Hao Zhang
article en

Abstract

Free-floating space manipulators are strongly coupled systems in which manipulator motion affects spacecraft base motion through momentum exchange, making simultaneous end-effector control and disturbance suppression challenging. This work investigates how an additional actuated prismatic degree of freedom influences whole-arm coordination in a 6R1P free-floating space manipulator. Compared with a fixed-length 6R configuration, the prismatic joint enlarges the feasible motion space and introduces an additional motion-allocation direction for full-pose tasks under generalized-Jacobian constraints. A proximal policy optimization (PPO)-based controller is developed for full-pose reaching with spacecraft-motion-aware objectives. Simulation results show that the 6R1P configuration improves reaching performance and reduces spacecraft reaction compared with the locked-prismatic 6R baseline. Trajectory-level dynamic reconstruction further reveals that the disturbance reduction is not caused by direct cancellation from the prismatic joint itself, but mainly by configuration-dependent redistribution of revolute-joint motions and enhanced mutual cancellation among their reaction contributions. These results demonstrate that telescopic redundancy provides a mechanism for coordinated motion allocation in free-floating manipulation, enabling learned policies to exploit additional degrees of freedom for improved task execution and reduced spacecraft disturbance.

MachinesVol. 14(10)
China Academy of Launch Vehicle Technology (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 8%
Space Satellite Systems and Control
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Redundant-Motion Coordination and Base Disturbance Suppression of a 6R1P Free-Floating Space Manipulator Based on Deep Reinforcement Learning — Jiaqi Duan, Zelin Yang, et al. · Machines (2026) | TGRS Research Map | TGRS