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Sub-1.5 Time-Optimal Multi-Robot Path Planning on Grids in Polynomial Time
[article]

2022

Graph-based multi-robot path planning (MRPP) is NP-hard to optimally solve. In this work, we propose the first low polynomial-time algorithm for MRPP achieving 1--1.5 asymptotic optimality guarantees on solution makespan for random instances under very high robot density. Specifically, on an $m_1\times m_2$ gird, $m_1 \ge m_2$, our RTH (Rubik Table with Highways) algorithm computes solutions for routing up to $\frac{m_1m_2}{3}$ robots with uniformly randomly distributed start and goal

doi:10.48550/arxiv.2201.08976
fatcat:2qlevmfaa5c2fh4tdofsuwerl4