发表机构
Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对动力下降中高度受限信息下的着陆点选择难题，提出基于模糊性收缩模型的风险规避评分与规划启发式，显著提升低尾着陆性能并大幅降低计算成本。
AI 中文摘要
在航空航天系统中，动力下降需要在细粒度危险直到低空才可分辨的情况下高效选择着陆点。这一过程构成了决策挑战，因为执行者必须在所有信息已知之前选择着陆点并做出相应行动。为了成功解决此问题，智能体必须对潜在风险进行推理，并在获得新观测时做出修正。我们引入了一个轻量级的高度受限信息模型，其中每个着陆点由平均分数和设计好的模糊性代理来概括，该代理随着飞行器下降并在高度-分辨率计划下于锥形足迹内感知而收缩。利用此抽象，我们推导出闭式、风险规避的着陆点评分技术（熵确定性等价和条件风险价值的高斯代理），并将其与贪婪和探索性规划器配对，以优先选择既高价值又对后期揭示的地形细节具有鲁棒性的着陆点。这些免回放的启发式方法相对于基于平均值的基线改善了低尾着陆结果（第1百分位数和确定性等价），当细化发生较晚且未解决细节较大时增益最大。我们还证明，这些方法的性能与蒙特卡洛树搜索基线相当或更好，且计算速度快数个数量级。我们的结果得到了数值模拟的支持。
英文摘要
In aerospace systems, powered descent requires efficiently selecting a landing site while fine-scale hazards remain unresolvable until low altitude. This process presents a decision challenge since the actor must select a site and make corresponding actions before all information is known. To successfully solve this problem, an agent must reason over potential risks and make corrections as new observations are made. We introduce a lightweight model of altitude-limited information where each landing site is summarized by a mean score and a designed ambiguity proxy that contracts as the vehicle descends and senses within a cone-shaped footprint under an altitude-to-resolution schedule. Using this abstraction, we derive closed-form, risk-averse site scoring techniques (an entropic certainty-equivalent and a Gaussian Conditional Value at Risk surrogate) and pair them with greedy and exploratory planners to prioritize sites that are both high-value and robust to late-revealed terrain detail. These rollout-free heuristics improve lower-tail landing outcomes (1st percentile and certainty-equivalent) relative to mean-based baselines, with the largest gains when refinement occurs late and unresolved detail is large. We also demonstrate that these methods perform comparably to or better than Monte Carlo Tree Search baselines with orders-of-magnitude faster computation. Our results are supported by numerical simulations.
