Explainable Multi-Robot Motion Planning via Segmentation

Justin Kottinger, Shaull Almagor, Morteza Lahijanian

Keywords: multi-robot motion planning, explainable artificial intelligence, safety-critical systems

TL;DR: This paper presents our progress on the developing a new class of multi-robot motion planning algorithms capable of explaining their solutions to human users.

Abstract: Multi-Robot Motion Planning (MRMP) is a fundamental problem in robotics and artificial intelligence (AI) where the goal is to calculate trajectories for multiple robots such that every vehicle safely reaches their respective goal when all trajectories are executed simultaneously. One limitation of current MRMP algorithms is their inability to explain their plans to human users. This limitation hinders MRMP algorithms’ use in safety-critical applications, such as air-traffic control, because a human cannot validate the plans. To this end, our study focuses on developing explainable MRMP algorithms that enable a human controller to efficiently validate automatically generated MRMP plans. We base explanations on the simplicity of visual human validation. Specifically, research shows that recognizing line intersections occurs very early in the cognitive process (namely in the primary visual cortex). Thus, MRMP can be explained using a collection of non-intersecting trajectory segments. To this end, we present two newly presented MRMP algorithms capable of automatically generating MRMP plans that can be satisfactorily explained to a human user.