  Several task and motion planning algorithms have been proposed recently to
design paths for mobile robot teams with collaborative high-level missions
specified using formal languages, such as Linear Temporal Logic (LTL). However,
the designed paths often lack reactivity to failures of robot capabilities
(e.g., sensing, mobility, or manipulation) that can occur due to unanticipated
events (e.g., human intervention or system malfunctioning) which in turn may
compromise mission performance. To address this novel challenge, in this paper,
we propose a new resilient mission planning algorithm for teams of
heterogeneous robots with collaborative LTL missions. The robots are
heterogeneous with respect to their capabilities while the mission requires
applications of these skills at certain areas in the environment in a
temporal/logical order. The proposed method designs paths that can adapt to
unexpected failures of robot capabilities. This is accomplished by
re-allocating sub-tasks to the robots based on their currently functioning
skills while minimally disrupting the existing team motion plans. We provide
experiments and theoretical guarantees demonstrating the efficiency and
resiliency of the proposed algorithm.
