HALO: Hazard-Aware Landing Optimization for Autonomous Systems

With autonomous aerial vehicles enact- ing 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, opti- mal landing trajectory generation, and contingency planning challenges encountered when landing in un- certain environments. Specifically, we develop and combine two novel algorithms, Hazard-Aware Land- ing 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 con- tingency planner that adaptively replans as new per- ception 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 non- adaptive DDTO approach.