Maximizing mutual information for multipass target search in changing environments.

Motion planning for multi-target autonomous search requires efficiently gathering as much information over an area as possible with an imperfect sensor. In disaster scenarios and contested environments the spatial connectivity may unexpectedly change (due to aftershock, avalanche, flood, building collapse, adversary movements, etc.) and the flight envelope may evolve as a known function of time to ensure rescue worker safety or to facilitate other mission goals. Algorithms designed to handle both expected and unexpected changes must: (1) reason over a sufficiently long time horizon to respect expected changes, and (2) replan quickly in response to unexpected changes. These ambitions are hindered by the submodularity property of mutual information, which makes optimal solutions NP-hard to compute. We present an algorithm for autonomous search in changing environments that uses a variety of techniques to improve both the speed and time horizon, including using e-admissible heuristics to speed up the search.