arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2008.03215eess.SYcs.SY
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基于强化学习的航天器自主六自由度对接机动

Autonomous Six-Degree-of-Freedom Spacecraft Docking Maneuvers via Reinforcement Learning

Charles E. Oestreich, Richard Linares, Ravi Gondhalekar

摘要:

六自由度对接机动的政策,通过强化学习和反馈控制律的实施。强化学习提供了一个潜在的框架,在不确定的环境中,具有低的板载计算成本的鲁棒性,自主机动。具体地,邻近策略优化用于产生在六自由度状态空间的一部分上有效的对接策略,同时努力使性能和控制成本最小化。使用模拟阿波罗换位和对接机动的实验展示了该政策的能力,并提供了与标准的最优控制技术的比较。此外,具体的挑战和解决办法,以及对对接政策的强化学习的好处和缺点的讨论,进行了讨论,以促进未来的研究。因此,这项工作将作为一个基础,为进一步研究基于学习的控制律航天器接近操作在不确定的环境。

英文摘要:

A policy for six-degree-of-freedom docking maneuvers is developed through reinforcement learning and implemented as a feedback control law. Reinforcement learning provides a potential framework for robust, autonomous maneuvers in uncertain environments with low on-board computational cost. Specifically, proximal policy optimization is used to produce a docking policy that is valid over a portion of the six-degree-of-freedom state-space while striving to minimize performance and control costs. Experiments using the simulated Apollo transposition and docking maneuver exhibit the policy's capabilities and provide a comparison with standard optimal control techniques. Furthermore, specific challenges and work-arounds, as well as a discussion on the benefits and disadvantages of reinforcement learning for docking policies, are discussed to facilitate future research. As such, this work will serve as a foundation for further investigation of learning-based control laws for spacecraft proximity operations in uncertain environments.

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。

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