解决航天器机械臂运动时的姿态不稳问题，实现精准操作与稳定控制。
Prior Policy Guided Dual-Agent Coordinated Manipulation Planning of Spacecraft-Manipulator System
- 用双智能体协同框架，结合先验策略引导强化学习。
- 任务成功率显著提升，控制精度优于基线方法，误差低于0.15°。
- 适用于复杂太空环境，对干扰和感知不确定性有强鲁棒性。
机械臂与基座之间的强动态耦合给航天器姿态稳定带来重大挑战，可能危及任务安全。本文提出双智能体协同操作规划（DACMP）框架，同时实现六自由度机械臂末端执行器的高精度位姿到达与基座航天器的姿态稳定。为提升学习效率，引入基于时间步级专家切换引导（TESG）机制的先验策略引导深度强化学习算法，促进全局收敛并提高任务成功率。大量实验表明，与基准DRL算法相比，DACMP在任务成功率和控制精度方面均有显著提升。此外，该方法在系统约束、环境扰动和感知不确定性等多重挑战场景下仍表现出优异鲁棒性。代码与仿真配置已开源：https://github.com/HIT-YuhuiHu/DACMP。
原文摘要 · Abstract (English)
The strong dynamic coupling between the manipulator and the base poses a significant challenge to maintaining spacecraft attitude stability, potentially compromising mission safety. In this paper, we propose a Dual-Agent Coordinated Manipulation Planning (DACMP) framework that simultaneously achieves high-precision end-effector pose reaching for a 6-DoF space manipulator and attitude stabilization of the base spacecraft. To enhance learning efficiency, we present a prior policy-guided Deep Reinforcement Learning algorithm incorporating the Timestep-level Expert Switching Guidance (TESG) mechanism, thereby promoting global convergence and improving task success rates. Extensive experiments demonstrate that DACMP significantly outperforms baseline DRL algorithms in terms of task success rate and control precision. Furthermore, the robustness of DACMP is validated under various challenging scenarios, including system constraints, environmental disturbances, and perception uncertainties. The code and simulation configurations are available on GitHub: https://github.com/HIT-YuhuiHu/DACMP.
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