This research proposes a new integrated framework for identifying safe landing locations and planning in-flight divert maneuvers. The state-of-the-art algorithms for landing zone selection utilize local terrain features such as slopes and roughness to judge the safety and priority of the landing point. However, when there are additional chances of observation and diverting in the future, these algorithms are not able to evaluate the safety of the decision itself to target the selected landing point considering the overall descent trajectory. In response to this challenge, we propose a reinforcement learning framework that optimizes a landing site selection strategy concurrently with a guidance and control strategy to the target landing site. The trained agent could evaluate and select landing sites with explicit consideration of the terrain features, quality of future observations, and control to achieve a safe and efficient landing trajectory at a system-level. The proposed framework was able to achieve 94.8 $\%$ of successful landing in highly challenging landing sites where over 80$\%$ of the area around the initial target lading point is hazardous, by effectively updating the target landing site and feedback control gain during descent.

中文翻译：

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同时考虑分流演习的深度强化学习，以安全选择降落地点

这项研究提出了一个新的集成框架，用于识别安全着陆位置并计划飞行中的转移操作。用于着陆区选择的最新算法利用诸如坡度和粗糙度之类的局部地形特征来判断着陆点的安全性和优先级。但是，当将来有更多观察和转移的机会时，这些算法不能考虑整个下降轨迹来评估以选定着陆点为目标的决策本身的安全性。为了应对这一挑战，我们提出了一个强化学习框架，该框架可以同时优化着陆点选择策略以及对目标着陆点的指导和控制策略。受过训练的特工可以在明确考虑地形特征，未来观测质量和控制的情况下评估和选择着陆点，以在系统级别上实现安全有效的着陆轨迹。拟议的框架通过有效地更新目标着陆点和反馈控制增益，能够在极具挑战性的着陆点实现成功着陆的94.8％，其中初始目标提货点周围超过80％的区域是危险的在下降期间。