Decentralized active robotic exploration and mapping for probabilistic field classification in environmental sensing

A central problem in environmental sensing and monitoring is to classify/label the hotspots in a large-scale environmental field. This paper presents a novel decentralized active robotic exploration (DARE) strategy for probabilistic classification/labeling of hotspots in a Gaussian process (GP)-based field. In contrast to existing state-of-the-art exploration strategies for learning environmental field maps, the time needed to solve the DARE strategy is independent of the map resolution and the number of robots, thus making it practical for in situ, real-time active sampling. Its exploration behavior exhibits an interesting formal trade-off between that of boundary tracking until the hotspot region boundary can be accurately predicted and wide-area coverage to find new boundaries in sparsely sampled areas to be tracked. We provide a theoretical guarantee on the active exploration performance of the DARE strategy: under reasonable conditional independence assumption, we prove that it can optimally achieve two formal cost-minimizing exploration objectives based on the misclassification and entropy criteria. Importantly, this result implies that the uncertainty of labeling the hotspots in a GP-based field is greatest at or close to the hotspot region boundaries. Empirical evaluation on real-world plankton density and temperature field data shows that, subject to limited observations, DARE strategy can achieve more superior classification of hotspots and time efficiency than state-of-the-art active exploration strategies.