A Successive-Elimination Approach to Adaptive Robotic Source Seeking

In this article, we study an adaptive source seeking problem, in which a mobile robot must identify the strongest emitter(s) of a signal in an environment with background emissions. Background signals may be highly heterogeneous and can mislead algorithms that are based on receding horizon control. We propose $\mathtt {AdaSearch}$ , a general algorithm for adaptive source seeking in the face of heterogeneous background noise. $\mathtt {AdaSearch}$ combines global trajectory planning with principled confidence intervals in order to concentrate measurements in promising regions while guaranteeing sufficient coverage of the entire area. Theoretical analysis shows that $\mathtt {AdaSearch}$ confers gains over a uniform sampling strategy when the distribution of background signals is highly variable. Simulation experiments demonstrate that when applied to the problem of radioactive source-seeking, $\mathtt {AdaSearch}$ outperforms both uniform sampling and a receding time horizon information-maximization approach based on the current literature. We also demonstrate $\mathtt {AdaSearch}$ in hardware, providing further evidence of its potential for real-time implementation.