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This paper addresses an autonomous exploration problem in which a mobilesensor, placed in a previously unseen search area, utilizes aninformation-theoretic navigation cost function to dynamically select the nextsensing action, i.e., location from which to take a measurement, to efficientlydetect and classify objects of interest within the area. Theinformation-theoretic cost function proposed in this paper consist of two\textit{information gain} terms, one for detection and localization of objectsand the other for sequential classification of the detected objects. We presenta novel closed-form representation for the cost function, derived from thedefinition of mutual information. We evaluate three different policies forchoosing the next sensing action: lawn mower, greedy, and non-greedy. For thesethree policies, we compare the results from our information-theoretic costfunctions to the results of other information-theoretic inspired costfunctions. Our simulation results show that search efficiency is greater usingthe proposed cost functions compared to those of the other methods, and thatthe greedy and non-greedy policies outperform the lawn mower policy.

Information-Theoretic Approach to Navigation for Efficient Detection and Classification of Underwater Objects