Autonomous Rendezvous and Docking (RVD) have been extensively studied in recent years, addressing the stringent requirements of spacecraft dynamics variations and the limitations of GNC systems. This paper presents an innovative approach employing Artificial Neural Networks (ANN) trained through Reinforcement Learning (RL) for autonomous spacecraft guidance and control during the final phase of the rendezvous maneuver. The proposed strategy is easily implementable onboard and offers fast adaptability and robustness to disturbances by learning control policies from experience rather than relying on predefined models. Extensive Monte Carlo simulations within a relevant environment are conducted in 6DoF settings to validate our approach, along with hardware tests that demonstrate deployment feasibility. Our findings highlight the efficacy of RL in assuring the adaptability and efficiency of spacecraft RVD, offering insights into future mission expectations.

翻译：自主交会对接（RVD）近年来得到了广泛研究，旨在应对航天器动力学变化的严格要求以及制导、导航与控制（GNC）系统的局限性。本文提出了一种创新方法，利用通过强化学习（RL）训练的人工神经网络（ANN），在交会机动的最终阶段实现自主航天器制导与控制。所提出的策略易于在星上实现，并通过从经验中学习控制策略而非依赖预定义模型，提供了对扰动的快速适应性和鲁棒性。我们在相关环境中进行了大量的六自由度（6DoF）蒙特卡洛仿真以验证该方法，同时通过硬件测试证明了其部署可行性。我们的研究结果凸显了强化学习在确保航天器RVD适应性和效率方面的有效性，并为未来任务预期提供了见解。