  Traditional multi-robot motion planning (MMP) focuses on computing
trajectories for multiple robots acting in an environment, such that the robots
do not collide when the trajectories are taken simultaneously. In
safety-critical applications, a human supervisor may want to verify that the
plan is indeed collision-free. In this work, we propose a notion of explanation
for a plan of MMP, based on visualization of the plan as a short sequence of
images representing time segments, where in each time segment the trajectories
of the agents are disjoint, clearly illustrating the safety of the plan. We
show that standard notions of optimality (e.g., makespan) may create conflict
with short explanations. Thus, we propose meta-algorithms, namely multi-agent
plan segmenting-X (MAPS-X) and its lazy variant, that can be plugged on
existing centralized sampling-based tree planners X to produce plans with good
explanations using a desirable number of images. We demonstrate the efficacy of
this explanation-planning scheme and extensively evaluate the performance of
MAPS-X.
