RL-AVIST：面向空间目标自主视觉检测的强化学习
RL-AVIST: Reinforcement Learning for Autonomous Visual Inspection of Space Targets
AI总结：
针对空间目标自主视觉检测，提出基于DreamerV3的强化学习框架RL-AVIST，在SRB中训练智能体，验证了基于模型方法在轨迹保真度和样本效率上的优势。
摘要：
对检测、维护和态势感知等自主在轨服务需求的日益增长，要求航天器具备在大型轨道目标周围执行复杂机动的能力。传统控制系统在适应性方面往往不足，尤其是在模型不确定性、多航天器配置或动态演变的任务背景下。本文介绍了RL-AVIST，一个用于空间目标自主视觉检测的强化学习框架。利用Space Robotics Bench（SRB），我们模拟了高保真度的六自由度航天器动力学，并使用DreamerV3（一种最先进的基于模型的强化学习算法）训练智能体，同时以PPO和TD3作为无模型基线。我们的研究聚焦于围绕月球门户（Lunar Gateway）和其他空间资产的3D近距离机动任务。我们在两种互补机制下评估任务性能：在随机速度向量上训练的通用智能体，以及在模拟已知检测轨道的固定轨迹上训练的专业智能体。此外，我们评估了策略在多种航天器形态和任务领域中的鲁棒性和泛化能力。结果表明，基于模型的强化学习在轨迹保真度和样本效率方面展现出有前景的能力，为未来太空操作的可扩展、可重训练控制解决方案铺平了道路。
英文摘要：
The growing need for autonomous on-orbit services such as inspection, maintenance, and situational awareness calls for intelligent spacecraft capable of complex maneuvers around large orbital targets. Traditional control systems often fall short in adaptability, especially under model uncertainties, multi-spacecraft configurations, or dynamically evolving mission contexts. This paper introduces RL-AVIST, a Reinforcement Learning framework for Autonomous Visual Inspection of Space Targets. Leveraging the Space Robotics Bench (SRB), we simulate high-fidelity 6-DOF spacecraft dynamics and train agents using DreamerV3, a state-of-the-art model-based RL algorithm, with PPO and TD3 as model-free baselines. Our investigation focuses on 3D proximity maneuvering tasks around targets such as the Lunar Gateway and other space assets. We evaluate task performance under two complementary regimes: generalized agents trained on randomized velocity vectors, and specialized agents trained to follow fixed trajectories emulating known inspection orbits. Furthermore, we assess the robustness and generalization of policies across multiple spacecraft morphologies and mission domains. Results demonstrate that model-based RL offers promising capabilities in trajectory fidelity, and sample efficiency, paving the way for scalable, retrainable control solutions for future space operations
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。
