Hazard Detection and Avoidance for Autonomous Spacecraft Landing

Abstract

Hazard Detection and Avoidance (HD&A) plays a pivotal role in enhancing the flexibility and diversity of mission designs and executions in planetary science and interplanetary development endeavors. HD&A encompasses two principal functionalities: hazard detection (HD) and hazard avoidance guidance, both of which are addressed and enhanced in this research. For the HD algorithm, a novel framework and formulation are introduced, leveraging machine learning and Gaussian random fields for improved efficiency and precision. Regarding hazard avoidance guidance, this research initiates by precisely defining and formulating this emergent challenge, subsequently presenting a set of solution frameworks tailored for this unique perception-aware guidance problem.