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arXiv · 1909.12216

Information-Guided Robotic Maximum Seek-and-Sample in Partially Observable Continuous Environments

Genevieve Flaspohler·Victoria Preston·Anna P. M. Michel·Yogesh Girdhar·Nicholas Roy
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Abstract

We present PLUMES, a planner to localizing and collecting samples at the global maximum of an a priori unknown and partially observable continuous environment. The "maximum-seek-and-sample" (MSS) problem is pervasive in the environmental and earth sciences. Experts want to collect scientifically valuable samples at an environmental maximum (e.g., an oil-spill source), but do not have prior knowledge about the phenomenon's distribution. We formulate the MSS problem as a partially-observable Markov decision process (POMDP) with continuous state and observation spaces, and a sparse reward signal. To solve the MSS POMDP, PLUMES uses an information-theoretic reward heuristic with continous-observation Monte Carlo Tree Search to efficiently localize and sample from the global maximum. In simulation and field experiments, PLUMES collects more scientifically valuable samples than state-of-the-art planners in a diverse set of environments, with various platforms, sensors, and challenging real-world conditions.

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Genevieve Flaspohler, Victoria Preston, Anna P. M. Michel, Yogesh Girdhar, Nicholas Roy. 2019-09-26. Information-Guided Robotic Maximum Seek-and-Sample in Partially Observable Continuous Environments. https://doi.org/10.1109/lra.2019.2929997

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