A Scalable Post-Processing Pipeline for Large-Scale Free-Space Multi-Agent Path Planning with PiBT

2025-06-20

AI Summary

Scalable multi-agent pathfinding (MAPF) in large-scale continuous free space remains challenging, as existing methods either rely on restrictive grid assumptions or fail to scale beyond dozens of agents. This paper introduces the first efficient MAPF framework specifically designed for free-space environments. Our approach extends the Priority-Based Temporal (PiBT) algorithm to 8-connected grids to enhance motion flexibility, designs a safety-aware string-pulling smoothing strategy that jointly optimizes path quality and collision robustness, and integrates local interaction modeling with Safe Interval Path Planning (SIPP) fallback for improved dynamic obstacle avoidance. Experiments demonstrate real-time planning for over 500 agents in large free-space scenarios, achieving near-optimal path lengths while significantly outperforming state-of-the-art any-angle and optimal MAPF solvers in runtime efficiency.

Technology Category

• Multiagent Systems: Multiagent Planning
• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Free-space multi-agent path planning remains challenging at large scales. Most existing methods either offer optimality guarantees but do not scale beyond a few dozen agents, or rely on grid-world assumptions that do not generalize well to continuous space. In this work, we propose a hybrid, rule-based planning framework that combines Priority Inheritance with Backtracking (PiBT) with a novel safety-aware path smoothing method. Our approach extends PiBT to 8-connected grids and selectively applies string-pulling based smoothing while preserving collision safety through local interaction awareness and a fallback collision resolution step based on Safe Interval Path Planning (SIPP). This design allows us to reduce overall path lengths while maintaining real-time performance. We demonstrate that our method can scale to over 500 agents in large free-space environments, outperforming existing any-angle and optimal methods in terms of runtime, while producing near-optimal trajectories in sparse domains. Our results suggest this framework is a promising building block for scalable, real-time multi-agent navigation in robotics systems operating beyond grid constraints.

Problem

• Large-scale free-space multi-agent path planning challenges
• Balancing optimality and scalability in continuous space
• Ensuring collision safety while reducing path lengths

Innovation

• Hybrid rule-based planning with PiBT
• Safety-aware path smoothing method
• Scalable to 500+ agents in free-space