With autonomous aerial vehicles enacting safety-critical missions, such as the Mars Science Laboratory Curiosity rover's landing on Mars, the tasks of automatically identifying and reasoning about potentially hazardous landing sites is paramount. This paper presents a coupled perception-planning solution which addresses the hazard detection, optimal landing trajectory generation, and contingency planning challenges encountered when landing in uncertain environments. Specifically, we develop and combine two novel algorithms, Hazard-Aware Landing Site Selection (HALSS) and Adaptive Deferred-Decision Trajectory Optimization (Adaptive-DDTO), to address the perception and planning challenges, respectively. The HALSS framework processes point cloud information to identify feasible safe landing zones, while Adaptive-DDTO is a multi-target contingency planner that adaptively replans as new perception information is received. We demonstrate the efficacy of our approach using a simulated Martian environment and show that our coupled perception-planning method achieves greater landing success whilst being more fuel efficient compared to a nonadaptive DDTO approach.

翻译：摘要：当自主飞行器执行如火星科学实验室好奇号火星车着陆等安全关键任务时，自动识别和推理潜在危险着陆点至关重要。本文提出了一种耦合感知-规划解决方案，用于应对在不确定环境中着陆时所面临的危险检测、最优着陆轨迹生成及应急规划挑战。具体而言，我们开发并组合两种新颖算法——风险感知着陆点选择（HALSS）与自适应延迟决策轨迹优化（Adaptive-DDTO），分别解决感知与规划难题。HALSS框架通过处理点云数据识别可行安全着陆区，而Adaptive-DDTO作为多目标应急规划器，能够根据新感知信息自适应地重新规划。我们通过在模拟火星环境中的实验验证了该方法的有效性，结果表明：与非自适应DDTO方法相比，本文提出的耦合感知-规划方法在实现更高着陆成功率的同时，具有更优的燃料效率。