Optimization of Edge Directions and Weights for Mixed Guidance Graphs in Lifelong Multi-Agent Path Finding

2026-03-02 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

This research introduces Mixed Guidance Graph Optimization (MGGO) to enhance Lifelong Multi-Agent Path Finding (LMAPF) by optimizing both edge directions and weights in guidance graphs. Traditional Guidance Graph Optimization (GGO) only optimizes edge weights, offering "soft" guidance that can still lead to head-on collisions, especially with realistic rotational motion models. The authors propose two MGGO methods: a two-phase approach that optimizes directions and weights separately, and a Quality Diversity (QD) based joint optimization using a neural network. They also develop Edge Reversal Search (ERS), a fast greedy algorithm to ensure strong connectivity in mixed guidance graphs. Experimental evaluations against state-of-the-art GGO methods and human-designed guidance graphs demonstrate that MGGO methods, particularly the QD-based joint optimization, achieve superior throughput in various LMAPF scenarios, especially for rule-based planners like PIBT, by reducing rotation actions.

Key takeaway

For AI Scientists and Research Scientists working on multi-agent pathfinding in dynamic environments like autonomous warehouses, incorporating mixed guidance graphs with optimized edge directions and weights is critical. Your current GGO methods may be insufficient for preventing head-on collisions and maximizing throughput, especially with rotational motion models. You should explore MGGO techniques, particularly QD-based joint optimization, to achieve state-of-the-art performance and reduce agent rotation actions, leading to more efficient and collision-free operations.

Key insights

Optimizing both edge directions and weights in guidance graphs significantly improves LMAPF throughput and collision avoidance.

Principles

• Strict guidance (edge directions) complements soft guidance (edge weights).
• Strong connectivity is crucial for effective directed guidance graphs.
• Diversity measures can assist objective optimization in complex search spaces.

Method

MGGO optimizes edge directions and weights using either a two-phase evolutionary algorithm or a QD-based neural network approach, with ERS ensuring strong connectivity.

In practice

• Implement ERS for fast strong connectivity repair in directed graphs.
• Consider QD algorithms for joint optimization of complex graph properties.
• Incorporate traffic patterns into guidance graph optimization models.

Topics

• Lifelong Multi-Agent Path Finding
• Mixed Guidance Graphs
• Guidance Graph Optimization
• Edge Direction Optimization
• Quality Diversity Algorithms

Best for: AI Scientist, Research Scientist, AI Researcher, AI Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.