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