Information-Theoretic Multi-Robot Adaptive Exploration and Mapping of Environmental Hotspot Fields

Abstract

Recent research in robot exploration and mapping has focused on sampling hotspot fields. This exploration task is formalized by [3] in a decision-theoretic planning framework called MAXP. The time complexity of solving MAXP approximately depends on the map resolution, which limits its use in large-scale, high-resolution exploration and mapping. To alleviate this computational difficulty, this paper presents an information-theoretic approach to MAXP (iMAXP); by reformulating the cost-minimizing iMAXP as a reward-maximizing problem, its time complexity becomes independent of map resolution and is less sensitive to increasing robot team size. Using the reward-maximizing dual, we derive a novel adaptive variant of maximum entropy sampling, thus improving the induced policy performance. We also demonstrate the superior performance of exploration policies for sampling the log-Gaussian process to that of policies for the Gaussian process in mapping the hotspot field. Lastly, we provide sufficient conditions that, when met, guarantee adaptivity has no benefit under an assumed environment model.