  This paper addresses the visibility-based pursuit-evasion problem where a
team of pursuer robots operating in a two-dimensional polygonal space seek to
establish visibility of an arbitrarily fast evader. This is a computationally
challenging task for which the best known complete algorithm takes time doubly
exponential in the number of robots. However, recent advances that utilize
sampling-based methods have shown progress in generating feasible solutions. An
aspect of this problem that has yet to be explored concerns how to ensure that
the robots can recover from catastrophic failures which leave one or more
robots unexpectedly incapable of continuing to contribute to the pursuit of the
evader. To address this issue, we propose an algorithm that can rapidly recover
from catastrophic failures. When such failures occur, a replanning occurs,
leveraging both the information retained from the previous iteration and the
partial progress of the search completed before the failure to generate a new
motion strategy for the reduced team of pursuers. We describe an implementation
of this algorithm and provide quantitative results that show that the proposed
method is able to recover from robot failures more rapidly than a baseline
approach that plans from scratch.
