地面测试床中用于多智能体检查的深度强化学习稳定性分析
Stability Analysis of Deep Reinforcement Learning for Multi-Agent Inspection in a Terrestrial Testbed
AI总结：
本研究评估了用于多智能体卫星检查的分层深度强化学习框架的稳定性与性能，通过在LINCS测试床上的实验验证了其有效弥合仿真到现实差距的能力。
摘要：
空间任务中自主系统的设计与部署需要稳健的解决方案，以应对严格的可靠性约束、延长的运行时间以及通信挑战。本研究评估了专为多智能体卫星检查任务设计的分层深度强化学习（DRL）框架的稳定性与性能。该框架将高层引导策略与低层运动控制器相结合，实现了可扩展的任务分配和高效的轨迹执行。在Local Intelligent Network of Collaborative Satellites（LINCS）测试床上进行的实验，评估了该框架在不同保真度水平下的性能，涵盖了从模拟环境到信息物理测试床。关键指标，包括任务完成率、移动距离和燃料消耗，突显了该框架的稳健性和适应性，尽管存在传感器噪声、动态扰动和运行时保证（RTA）约束等现实世界的不确定性。结果表明，该分层控制器有效弥合了仿真到现实的差距，在适应现实环境复杂性的同时保持了高任务完成率。这些发现验证了该框架在未来空间任务中实现自主卫星操作的潜力。
英文摘要：
The design and deployment of autonomous systems for space missions require robust solutions to navigate strict reliability constraints, extended operational duration, and communication challenges. This study evaluates the stability and performance of a hierarchical deep reinforcement learning (DRL) framework designed for multi-agent satellite inspection tasks. The proposed framework integrates a high-level guidance policy with a low-level motion controller, enabling scalable task allocation and efficient trajectory execution. Experiments conducted on the Local Intelligent Network of Collaborative Satellites (LINCS) testbed assess the framework's performance under varying levels of fidelity, from simulated environments to a cyber-physical testbed. Key metrics, including task completion rate, distance traveled, and fuel consumption, highlight the framework's robustness and adaptability despite real-world uncertainties such as sensor noise, dynamic perturbations, and runtime assurance (RTA) constraints. The results demonstrate that the hierarchical controller effectively bridges the sim-to-real gap, maintaining high task completion rates while adapting to the complexities of real-world environments. These findings validate the framework's potential for enabling autonomous satellite operations in future space missions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
