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We study an adaptive source seeking problem, in which a mobile robot mustidentify the strongest emitter(s) of a signal in an environment with backgroundemissions. Background signals may be highly heterogeneous and can misleadalgorithms that are based on receding horizon control. We propose AdaSearch, ageneral algorithm for adaptive source seeking in the face of heterogeneousbackground noise. AdaSearch combines global trajectory planning with principledconfidence intervals in order to concentrate measurements in promising regionswhile guaranteeing sufficient coverage of the entire area. Theoretical analysisshows that AdaSearch confers gains over a uniform sampling strategy when thedistribution of background signals is highly variable. Simulation experimentsdemonstrate that when applied to the problem of radioactive source seeking,AdaSearch outperforms both uniform sampling and a receding time horizoninformation-maximization approach based on the current literature. We alsodemonstrate AdaSearch in hardware, providing further evidence of its potentialfor real-time implementation.

A Successive-Elimination Approach to Adaptive Robotic Sensing