Robust multi-agent path finding and executing

Dor Atzmon, Roni Stern, Ariel Felner, Glenn Wagner, Roman Barták, Neng Fa Zhou

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Multi-agent path-finding (MAPF) is the problem of finding a plan for moving a set of agents from their initial locations to their goals without collisions. Following this plan, however, may not be possible due to unexpected events that delay some of the agents. In this work, we propose a holistic solution for MAPF that is robust to such unexpected delays. First, we introduce the notion of a k-robust MAPF plan, which is a plan that can be executed even if a limited number (k) of delays occur. We propose sufficient and required conditions for finding a k-robust plan, and show how to convert several MAPF solvers to find such plans. Then, we propose several robust execution policies. An execution policy is a policy for agents executing a MAPF plan. An execution policy is robust if following it guarantees that the agents reach their goals even if they encounter unexpected delays. Several classes of such robust execution policies are proposed and evaluated experimentally. Finally, we present robust execution policies for cases where communication between the agents may also be delayed. We performed an extensive experimental evaluation in which we compared different algorithms for finding robust MAPF plans, compared different robust execution policies, and studied the interplay between having a robust plan and the performance when using a robust execution policy.

Original languageEnglish
Pages (from-to)549-579
Number of pages31
JournalJournal of Artificial Intelligence Research
Volume67
DOIs
StatePublished - 12 Mar 2020

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