Decoupling Geometric Planning and Execution in Scalable Multi-Agent Path Finding

2026-03-11

AI Summary

This work addresses the scalability limitations of large-scale multi-agent path planning, which typically relies on time-expanded graphs and centralized conflict resolution. The authors propose a hybrid prioritized framework that explicitly decouples geometric path planning from execution for the first time. Spatial paths are sequentially generated on the original graph using A* augmented with geometric conflict prediction (GCP), followed by decentralized local controllers (DLCs) that dynamically insert wait actions via vertex-level FIFO permission queues to resolve runtime conflicts. Notably, the approach avoids explicit temporal modeling, scales to up to 1,000 agents on standard benchmark maps with near-linear runtime growth, and achieves 100% success rate in scenarios satisfying geometric feasibility assumptions.

Abstract

Multi-Agent Path Finding (MAPF) requires collision-free trajectories for multiple agents on a shared graph, often with the objective of minimizing the sum-of-costs (SOC). Many optimal and bounded-suboptimal solvers rely on time-expanded models and centralized conflict resolution, which limits scalability in large or dense instances. We propose a hybrid prioritized framework that separates geometric planning from execution-time conflict resolution. In the first stage, Geometric Conflict Preemption (GCP) plans agents sequentially with A* on the original graph while inflating costs for transitions entering vertices used by higher-priority paths, encouraging spatial detours without explicit time reasoning. In the second stage, a Decentralized Local Controller (DLC) executes the geometric paths using per-vertex FIFO authorization queues and inserts wait actions only when required to avoid vertex and edge-swap conflicts. Experiments on standard benchmark maps with up to 1000 agents show that the method scales with an empirically near-linear runtime trend and attains a 100% success rate on instances satisfying the geometric feasibility assumption. On bottleneck-heavy maps, GCP reduces synchronization-induced waiting and often improves SOC on bottleneck-heavy maps

Problem

• Multi-Agent Path Finding
• scalability
• collision-free trajectories
• time-expanded models
• centralized conflict resolution

Innovation

• Geometric Conflict Preemption
• Decentralized Local Controller
• Multi-Agent Path Finding
• Scalable Planning
• Decoupled Execution