TY - JOUR
T1 - Exploiting Geometric Constraints in Multi-Agent Pathfinding
AU - Atzmon, Dor
AU - Bernardini, Sara
AU - Fagnani, Fabio
AU - Fairbairn, David
N1 - Funding Information:
For the purpose of open access, the author(s) has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising. This work is partially supported by Innovate UK (VersaTile Grant - 10005401), Leverhulme Trust (Grant VP1-2019-037), and MIUR (Grant CUP: E11G18000350001).
Publisher Copyright:
Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - In tackling the multi-agent pathfinding problem (MAPF), we study a specific class of paths that are constructed by taking the agents' shortest paths from the start to the goal locations and adding safe delays at the beginning of the paths, which guarantee that they are non-conflicting. Safe delays are calculated by exploiting a set of fundamental geometric constraints among the distances between all agents' start and goal locations. Those constraints are simple, but the MAPF problem reformulated in terms of them remains computationally hard. Nonetheless, based on safe delays, we devise a new, fast and lightweight algorithm, called Delayed Shortest Path (DSP), to find solutions to the MAPF problem. Via an extensive experimental evaluation on standard benchmarks, we show that, in many cases, our technique runs several orders of magnitudes faster than related methods while addressing problems with thousands of agents and returning low-cost solutions.
AB - In tackling the multi-agent pathfinding problem (MAPF), we study a specific class of paths that are constructed by taking the agents' shortest paths from the start to the goal locations and adding safe delays at the beginning of the paths, which guarantee that they are non-conflicting. Safe delays are calculated by exploiting a set of fundamental geometric constraints among the distances between all agents' start and goal locations. Those constraints are simple, but the MAPF problem reformulated in terms of them remains computationally hard. Nonetheless, based on safe delays, we devise a new, fast and lightweight algorithm, called Delayed Shortest Path (DSP), to find solutions to the MAPF problem. Via an extensive experimental evaluation on standard benchmarks, we show that, in many cases, our technique runs several orders of magnitudes faster than related methods while addressing problems with thousands of agents and returning low-cost solutions.
UR - http://www.scopus.com/inward/record.url?scp=85169785210&partnerID=8YFLogxK
U2 - 10.1609/icaps.v33i1.27174
DO - 10.1609/icaps.v33i1.27174
M3 - Conference article
AN - SCOPUS:85169785210
SN - 2334-0835
VL - 33
SP - 17
EP - 25
JO - Proceedings International Conference on Automated Planning and Scheduling, ICAPS
JF - Proceedings International Conference on Automated Planning and Scheduling, ICAPS
IS - 1
T2 - 33rd International Conference on Automated Planning and Scheduling, ICAPS 2023
Y2 - 8 July 2023 through 13 July 2023
ER -