Subsurface Sampling Robot for Time-limited Asteroid Exploration

Abstract

This paper presents a novel approach to sampling subsurface asteroidal regolith under severe time constraints. Sampling operations that must be completed within a few hours require techniques that can manage subsurface obstructions that may be encountered. The large uncertainties due to our lack of knowledge of regolith properties also make sampling difficult. To aid in managing these challenges, machine learning-based detection methods using tactile feedback can detect the presence of rocks deeper than the length of the probe, ensuring reliable sampling in unobstructed areas. In addition, given the variability of soil hardness and the short time available, a corer shooting mechanism has been developed that uses a special shape-memory alloy to collect regolith in about a minute. Experiments on subsurface obstacle detection and shooting-corer ejection tests were conducted to demonstrate the functionality of this approach.