Prioritised Planning with Guarantees

Jonathan Morag, Yue Zhang, Daniel Koyfman, Zhe Chen, Ariel Felner, Daniel Harabor, Roni Stern

Research output: Contribution to journalConference articlepeer-review


Prioritised Planning (PP) is a family of incomplete and suboptimal algorithms for multi-agent and multi-robot navigation. In PP, agents compute collision-free paths in a fixed order, one at a time. Although fast and usually effective, PP can still fail, leaving users without explanation or recourse. In this work, we give a theoretical and empirical basis for better understanding the underlying problem solved by PP, which we call Priority Constrained MAPF (PC-MAPF). We first investigate the complexity of PC-MAPF and show that the decision problem is NP-hard. We then develop Priority Constrained Search (PCS), a new algorithm that is both complete and optimal with respect to a fixed priority ordering. We experiment with PCS in a range of settings, including comparisons with existing PP baselines, and we give first-known results for optimal PC-MAPF on a popular benchmark set.

Original languageEnglish
Pages (from-to)82-90
Number of pages9
JournalThe International Symposium on Combinatorial Search
Issue number1
StatePublished - 2024
Externally publishedYes
Event17th International Symposium on Combinatorial Search, SoCS 2024 - Kananaskis, Canada
Duration: 6 Jun 20248 Jun 2024

Bibliographical note

Publisher Copyright:
© 2024, Association for the Advancement of Artificial Intelligence ( All rights reserved.


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