Speedup Techniques for Switchable Temporal Plan Graph Optimization

2024-12-20

AI Summary

To address deadlock and collision issues in Multi-Agent Path Finding (MAPF) caused by unexpected delays, this paper proposes a switchable temporal planning graph optimization framework that ensures conflict-free and deadlock-free execution under delay robustness. The method introduces four synergistic acceleration techniques—strongly admissible heuristics, edge grouping, priority-based branching, and incremental solving—unified within both GSES and MILP solvers. Experiments across four benchmark map classes demonstrate that our approach achieves over twice the task success rate of GSES and up to 30× faster optimal solution times. These improvements significantly enhance the feasibility of real-time re-planning in dynamic environments.

Technology Category

• Planning, Routing, and Scheduling: Temporal Planning
• Multiagent Systems: Multiagent Planning
• Search and Optimization: Heuristic Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Multi-Agent Path Planning
• Sudden Stop
• Optimization Speed

Innovation

• Improved GSES Method
• Optimized Problem Decomposition
• Enhanced Efficiency and Success Rate