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In search and surveillance applications in robotics, it is intuitive tospatially distribute robot trajectories with respect to the probability oflocating targets in the domain. Ergodic coverage is one such approach totrajectory planning in which a robot is directed such that the percentage oftime spent in a region is in proportion to the probability of locating targetsin that region. In this work, we extend the ergodic coverage algorithm torobots operating in constrained environments and present a formulation that cancapture sensor footprint and avoid obstacles and restricted areas in thedomain. We demonstrate that our formulation easily extends to coordination ofmultiple robots equipped with different sensing capabilities to perform ergodiccoverage of a domain.

Ergodic Coverage In Constrained Environments Using Stochastic Trajectory Optimization