Path Planning and Reinforcement Learning-Driven Control of On-Orbit Free-Flying Multi-Arm Robots

2026-03-24

AI Summary

This work proposes a synergistic control framework that integrates trajectory optimization (TO) with model-free deep reinforcement learning (RL) to address the challenges of motion coupling, environmental disturbances, and dynamic control faced by on-orbit free-flying multi-arm robots in complex space environments. The approach uniquely employs TO for joint planning of end-effector trajectories and thruster thrust profiles, generating dynamically and kinematically feasible, high-efficiency trajectories. Concurrently, RL enables model-free adaptive tracking in high-dimensional action spaces, effectively compensating for dynamic mismatches and uncertainties. By incorporating thruster-assisted spacecraft attitude control, the method substantially reduces the stabilization burden on manipulators, enhancing system redundancy and task safety. Simulation results demonstrate superior performance over conventional strategies in both surface-contact and free-approach representative tasks, significantly improving motion smoothness, operational efficiency, and overall robustness.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This paper presents a hybrid approach that integrates trajectory optimization (TO) and reinforcement learning (RL) for motion planning and control of free-flying multi-arm robots in on-orbit servicing scenarios. The proposed system integrates TO for generating feasible, efficient paths while accounting for dynamic and kinematic constraints, and RL for adaptive trajectory tracking under uncertainties. The multi-arm robot design, equipped with thrusters for precise body control, enables redundancy and stability in complex space operations. TO optimizes arm motions and thruster forces, reducing reliance on the arms for stabilization and enhancing maneuverability. RL further refines this by leveraging model-free control to adapt to dynamic interactions and disturbances. The experimental results validated through comprehensive simulations demonstrate the effectiveness and robustness of the proposed hybrid approach. Two case studies are explored: surface motion with initial contact and a free-floating scenario requiring surface approximation. In both cases, the hybrid method outperforms traditional strategies. In particular, the thrusters notably enhance motion smoothness, safety, and operational efficiency. The RL policy effectively tracks TO-generated trajectories, handling high-dimensional action spaces and dynamic mismatches. This integration of TO and RL combines the strengths of precise, task-specific planning with robust adaptability, ensuring high performance in the uncertain and dynamic conditions characteristic of space environments. By addressing challenges such as motion coupling, environmental disturbances, and dynamic control requirements, this framework establishes a strong foundation for advancing the autonomy and effectiveness of space robotic systems.

Problem

• on-orbit servicing
• free-flying multi-arm robots
• motion coupling
• environmental disturbances
• dynamic control

Innovation

• trajectory optimization
• reinforcement learning
• multi-arm space robot
• on-orbit servicing
• hybrid control