HALO: Hazard-Aware Landing Optimization for Autonomous Systems

• URL: http://arxiv.org/abs/2304.01583v1
• Date: Tue, 4 Apr 2023 07:20:06 GMT
• Title: HALO: Hazard-Aware Landing Optimization for Autonomous Systems
• Authors: Christopher R. Hayner, Samuel C. Buckner, Daniel Broyles, Evelyn Madewell, Karen Leung and Behcet Acikmese
• Abstract summary: This paper presents a coupled perception-planning solution which addresses the hazard detection, optimal landing trajectory generation, and contingency planning challenges. We develop and combine two novel algorithms, Hazard-Aware Landing Site Selection (HALSS) and Adaptive Deferred-Decision Trajectory Optimization (-DDTO), to address the perception and planning challenges. We demonstrate the efficacy of our approach using a simulated Martian environment and show that our coupled perception-planning method achieves greater landing success.
• Score: 1.5414037351414311
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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