  We address the challenge of computing search paths in real-time for subsea
applications where the goal is to locate an unknown number of targets on the
seafloor. Our approach maximizes a formal definition of search effectiveness
given finite search effort. We account for false positive measurements and
variation in the performance of the search sensor due to geographic variation
of the seafloor. We compare near-optimal search paths that can be computed in
real-time with optimal search paths for which real-time computation is
infeasible. We show how sonar data acquired for locating targets at a specific
location can also be used to characterize the performance of the search sonar
at that location. Our approach is illustrated with numerical experiments where
search paths are planned using sonar data previously acquired from Boston
Harbor.

Robotics;Artificial Intelligence