  In this paper, we propose a method to replan coverage paths for a robot
operating in an environment with initially unknown static obstacles. Existing
coverage approaches reduce coverage time by covering along the minimum number
of coverage lines (straight-line paths). However, recomputing such paths online
can be computationally expensive resulting in robot stoppages that increase
coverage time. A naive alternative is greedy detour replanning, i.e.,
replanning with minimum deviation from the initial path, which is efficient to
compute but may result in unnecessary detours. In this work, we propose an
anytime coverage replanning approach named OARP-Replan that performs
near-optimal replans to an interrupted coverage path within a given time
budget. We do this by solving linear relaxations of integer linear programs
(ILPs) to identify sections of the interrupted path that can be optimally
replanned within the time budget. We validate OARP-Replan in simulation and
perform comparisons against a greedy detour replanner and other
state-of-the-art coverage planners. We also demonstrate OARP-Replan in
experiments using an industrial-level autonomous robot.
