posted on 2005-10-01, 00:00authored byKian Hsiang Low, Geoffrey J. Gordon, John M. Dolan, Pradeep K. Khosla
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 argues
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 areas
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 obvious
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 mineral
density estimators, which have lower error than previously-developed estimators;
they are derived from the older estimators via a process called Rao-Blackwellization.
Since the efficiency of estimators depends on the type of mineralogical population sampled,
the population characteristics that favor ACS estimators are also analyzed. The
ACS scheme and our new estimators are evaluated empirically in a detailed simulation
of the prospecting task, and the quantitative results show that our approach can yield
more minerals with less resources and provide more accurate mineral density estimates
than previous methods.