[Thesis Proposal] Predictive Exploration for Autonomous Science

Speaker: David Thompson

Abstract:
Planetary science is entering a new era in which exploration robots can outrun their own ability to collect science data. Autonomous navigation will soon permit single-command traverses of multiple kilometers. Nevertheless, the time for taking measurements and the bandwidth available for transmitting them to Earth will remain relatively constant. A growing body of research addresses these bottlenecks with onboard data understanding. Autonomous rovers can use pattern recognition, learning and planning technologies to place instruments and take measurements without human supervision. These robots autonomously choose the most important features to observe and transmit, traveling longer distances without sacrificing our understanding of the visited terrain.

I argue that intelligent explorer agents must exploit structure in their environment. In other words, they must be mapmakers. Maps can represent spatial structure (similarities from one locale to the next) and inter-sensor structure (correlations between different sensing modes). “Predictive exploration” formulates mapmaking as an experimental design problem. Generative spatial models guide the agent to informative areas while minimizing redundant measurements. Information gain over the map determines exploration decisions, while a similar criterion suggests the best data products for downlink. We will demonstrate these principles with a rover system that autonomously builds kilometer-scale geologic maps.

A copy of the thesis proposal document:
http://www.cs.cmu.edu/~drt/ThompsonProposal.pdf.