Environmental Information Improves Robotic Search Performance

We address the problem where a mobile search agent seeks to find an unknown number of stationary objects distributed in a bounded search domain, and the search mission is subject to time/distance constraint. Our work accounts for false positives, false negatives and environmental uncertainty. We consider the case that the performance of a search sensor is dependent on the environment (e.g., clutter density), and therefore sensor performance is better in some locations than in others. For applications where environmental information can be acquired, we derive a decision-theoretic cost function to compute the locations where the environmental information should be acquired. We address the cases where environmental characterization is performed either by a separate vehicle or by the same vehicle that performs the search task.