Speedup Techniques for Switchable Temporal Plan Graph Optimization

TL;DR

This paper presents Improved GSES, an enhanced algorithm for optimizing switchable temporal plan graphs in multi-agent pathfinding, achieving significant speedups and higher success rates over previous methods.

Contribution

It introduces four novel speedup techniques for GSES, greatly improving efficiency and success rates in optimizing switchable temporal plan graphs under delays.

Findings

Improved GSES achieves over twice the success rate of GSES.

Up to 30-fold speedup on various MAPF instances.

Consistent performance improvements across different map types.

Abstract

Multi-Agent Path Finding (MAPF) focuses on planning collision-free paths for multiple agents. However, during the execution of a MAPF plan, agents may encounter unexpected delays, which can lead to inefficiencies, deadlocks, or even collisions. To address these issues, the Switchable Temporal Plan Graph provides a framework for finding an acyclic Temporal Plan Graph with the minimum execution cost under delays, ensuring deadlock- and collision-free execution. Unfortunately, existing optimal algorithms, such as Mixed Integer Linear Programming and Graph-Based Switchable Edge Search (GSES), are often too slow for practical use. This paper introduces Improved GSES, which significantly accelerates GSES through four speedup techniques: stronger admissible heuristics, edge grouping, prioritized branching, and incremental implementation. Experiments conducted on four different map types with varying numbers of agents demonstrate that Improved GSES consistently achieves over twice the success rate of GSES and delivers up to a 30-fold speedup on instances where both methods successfully find solutions.