Adaptive Sampling Site Selection for Robotic Exploration in Unknown Environments

Autonomously selecting the right sequence of locations to sample is critical during exploration missions in unknown environments, with constraints on the number of samples that can be collected, and a possibility of system failure. A key idea for decision-making in unknown environments is to exploit side information available to the agent, combined with the information gained from samples collected so far, to estimate the sampling values. In this paper, we pose the problem of sampling site selection as a problem of finding the optimal policy in a Markov decision process modeling the unknown sampling values and the outcomes associated with sampling attempts at different locations. Our solution exploits the fact that the partially unknown rewards of this Markov decision process are correlated to each other to devise a strategy that attempts to maximize the total sample value while also ensuring that the agent achieves its minimum mission requirement. We validate the utility of the proposed approach by evaluating the method against a baseline strategy that pursues collecting the samples that are estimated to be of the highest value. Our evaluations use a simulated sampling problem on Martian terrain and using OceanWATERS, a high-fidelity simulator of a future Europa lander mission.