Adaptive sampling for multi-robot wide area prospecting,” Robotics Institute

Prospecting for in situ mineral resources is essential for establishing settlements on the Moon and Mars. To reduce human effort and risk, it is desirable to build robotic systems to perform this prospecting. An important issue in designing such systems is the sampling strategy: how do the robots choose where to prospect next? This paper ar-gues that a strategy called Adaptive Cluster Sampling (ACS) has a number of desirable properties: compared to conventional strategies, (1) it reduces the total mission time and energy consumption of a team of robots, and (2) returns a higher mineral yield and more information about the prospected region by directing exploration towards ar-eas of high mineral density, thus providing detailed maps of the boundaries of such areas. Due to the adaptive nature of the sampling scheme, it is not immediately obvi-ous how the resulting sampled data can be used to provide an unbiased, low-variance estimate of the regional mineral density. This paper therefore investigates new min-eral density estimators, which have lower error than previously-developed estimators; they are derived from the older estimators via a process called Rao-Blackwellization