Hazard-Aware Landing Optimization

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 non-adaptive DDTO approach.

BibTeX

@INPROCEEDINGS{10160655,
        author={Hayner, Christopher R. and Buckner, Samuel C. and Broyles, Daniel and Madewell, Evelyn and Leung, Karen and Açikmeşe, Behçet},
        booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)}, 
        title={HALO: Hazard-Aware Landing Optimization for Autonomous Systems}, 
        year={2023},
        volume={},
        number={},
        pages={3261-3267},
        keywords={Space vehicles;Point cloud compression;Autonomous systems;Navigation;Contingency management;Real-time systems;Planning},
        doi={10.1109/ICRA48891.2023.10160655}}