Some search problems have an additional dimension which is seeking OOIs while attempting to conceal the search agents’ location from the OOIs. This is applicable to reconnaissance settings wherein the safety of the search agents can be compromised. Prior work usually focuses on adversarial search settings where the hiders (OOIs) are actively trying to evade the seekers (search agents), however most approaches assume unrealistic parameters such as complete knowledge, infinite travel speed, unlimited compute and/or perfect observation models. We model the problem as a multi-objective optimization over the potential information gain of taking an action and the risk of information leakage to an unknown number of OOIs with unknown locations.  We present the Stealthy Topography-Aware Reconnaissance (STAR) algorithm, a multi-objective parallelized Thompson Sampling based algorithm that relies on a strong topographical prior to reason over changing visibility risk over the course of the search.

In both subproblems defined above, we show through simulation experiments that GUTS and STAR consistently outperform existing baseline methods in their respective problems. We conduct field tests using our multi-robot system in an unstructured environment with a search area of varying scale (0.075 sq. km – 2.6 sq. km). Our system demonstrates robustness to various failure modes, achieving full recovery of OOIs (where feasible) in every field test.