Abstract
The task of seeking an unknown source with the measurements of the emitted signal strength is named as the source-exploration problem, whose challenging issues with conducting autonomous source-exploration are the lack of $a~priori$ knowledge about the distribution of the emitted signal and the presence of unignorable noise in both the propagation situation and equipped sensor readings. This paper presents a planner for an autonomous robot engaged in seeking for an odor source. The guidance strategy for the planner is based on a control-theoretic paradigm which incorporates an optimization technique into the structure. The control law is inspired by two prominent behaviors widely observed in biology, namely, chemotaxis and anemotaxis. The two behaviors are formulated in the control development. The derived control law is rigorously analyzed and guarantees the converge of a nonlinear under-actuated robot to reach the unknown source, which provides a case study on using run-time optimization to guide rigorous control algorithm development. Besides theoretical proof, our results are also validated in numerical simulations and physical experiments.
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