Safe Obstacle-Free Guidance of Space Manipulators in Debris Removal Missions via Deep Reinforcement Learning

2025/10/08 by Vincent Lam, Lam, Vincent, Robin Chhabra +1
Engineering · Physics and Astronomy · #Astro and Planetary Science #FOS: Computer and information sciences #Robotics (cs.RO) #Space Satellite Systems and Control #Spacecraft Dynamics and Control

paper · pdf · doi:10.48550/arxiv.2510.06566

openalex publication_date 2025/10/08 · openalex created_date 2025/10/18 · openalex updated_date 2026/07/28

Abstract

The objective of this study is to develop a model-free workspace trajectory planner for space manipulators using a Twin Delayed Deep Deterministic Policy Gradient (TD3) agent to enable safe and reliable debris capture. A local control strategy with singularity avoidance and manipulability enhancement is employed to ensure stable execution. The manipulator must simultaneously track a capture point on a non-cooperative target, avoid self-collisions, and prevent unintended contact with the target. To address these challenges, we propose a curriculum-based multi-critic network where one critic emphasizes accurate tracking and the other enforces collision avoidance. A prioritized experience replay buffer is also used to accelerate convergence and improve policy robustness. The framework is evaluated on a simulated seven-degree-of-freedom KUKA LBR iiwa mounted on a free-floating base in Matlab/Simulink, demonstrating safe and adaptive trajectory generation for debris removal missions.

Citations

• Prioritized Experience Replay
• Reaction null-space control of flexible structure mounted manipulator systems
• The kinematics, dynamics, and control of free-flying and free-floating space robotic systems
• Resolved motion rate control of space manipulators with generalized Jacobian matrix
• Real-Time Obstacle Avoidance for Manipulators and Mobile Robots

Related

• A Learning System for Motion Planning of Free-Float Dual-Arm Space Manipulator towards Non-Cooperative Object
• Learning-Aided Control of Robotic Tether-Net with Maneuverable Nodes to Capture Large Space Debris
• Trajectory Planning for Teleoperated Space Manipulators Using Deep Reinforcement Learning
• DK-RRT: Deep Koopman RRT for Collision-Aware Motion Planning of Space Manipulators in Dynamic Debris Environments
• Improving Soft-Capture Phase Success in Space Debris Removal Missions: Leveraging Deep Reinforcement Learning and Tactile Feedback