Ergodic Coverage In Constrained Environments Using Stochastic Trajectory Optimization
This work addresses trajectory planning for robots in constrained settings, which is an incremental improvement over existing ergodic coverage methods.
The paper tackles the problem of planning robot trajectories for search and surveillance in constrained environments by extending the ergodic coverage algorithm to handle obstacles, restricted areas, and sensor footprints, and demonstrates its applicability to multi-robot coordination with different sensing capabilities.
In search and surveillance applications in robotics, it is intuitive to spatially distribute robot trajectories with respect to the probability of locating targets in the domain. Ergodic coverage is one such approach to trajectory planning in which a robot is directed such that the percentage of time spent in a region is in proportion to the probability of locating targets in that region. In this work, we extend the ergodic coverage algorithm to robots operating in constrained environments and present a formulation that can capture sensor footprint and avoid obstacles and restricted areas in the domain. We demonstrate that our formulation easily extends to coordination of multiple robots equipped with different sensing capabilities to perform ergodic coverage of a domain.