论文标题
MAPS-X：可解释的多机器人运动计划通过分割
MAPS-X: Explainable Multi-Robot Motion Planning via Segmentation
论文作者
论文摘要
传统的多机器人运动计划（MMP）着重于在环境中起作用的多个机器人的计算轨迹，因此当同时采用轨迹时，机器人不会碰撞。在关键安全应用中，人类主管可能想验证该计划确实没有碰撞。在这项工作中，我们提出了一个对MMP计划的解释概念，基于对计划的可视化，作为代表时间段的简短图像序列，在每个时间段中，代理的轨迹都不相交，清楚地说明了计划的安全性。我们表明，最优性的标准概念（例如MakePan）可能会与简短的解释造成冲突。因此，我们提出了元算法，即多代理计划分割-X（MAPS-X）及其懒惰变体，可以插入现有的基于集中抽样的树计划者X上，以制作有大量图像数量的良好解释的计划。我们证明了这种解释计划方案的功效，并广泛评估了MAPS-X的性能。
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.
