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Statistical hazard detection using shadows for small robotic landers/hoppers

Small robotic vehicles are limited in mass and computational resources. To enable future missions, new methods that provide hazard detection while reducing the mass and computational load from complex sensors and algorithms will provide cost effective solutions for safe landing. This paper describes a technique for hazard detection using shadows and statistical methods to determine the most likely safe landing aim point during traverse and approach to landing at a new surface location. The number and size of shadows is used to statistically determine the safest locations. This method assumes that higher density of shadows infers greater roughness, and larger shadows infer larger hazards. This simpler statistical shadowing hazard detection technique can be used to enable smaller lander/hopper missions which are restricted in their sensor payloads and which traverse across a planet's surface at low altitudes. This allows hazard detection to begin at large distances and shallow path angles. The low computational resources needed for this technique enables constant reassessment of potential landing sites during the duration of traverse from one location to another. This paper presents analysis showing the performance of this method under varying approach angles. The method for hazard detection is compared to techniques proposed for conventional landers. We discuss how this method can be used for hopper missions.