Planning over MAPF Agent Dependencies via Multi-Dependency PIBT

2026-03-24

AI Summary

This work addresses the limitations of existing Multi-Agent Path Finding (MAPF) algorithms—such as PIBT and EPIBT—which rely on rule-based mechanisms that handle only single-dependency conflicts and struggle in high-density, large-scale scenarios with complex inter-agent dependencies. To overcome this, we propose the Multi-Dependency PIBT (MD-PIBT) framework, which formally defines agent dependencies and unifies PIBT and EPIBT within a generalized model capable of generating a broader range of planning strategies. By incorporating multi-dependency priority inheritance and backtracking mechanisms, along with parameterized configuration, MD-PIBT efficiently resolves path planning for large numbers of homogeneous agents under kinematic constraints in dense environments. Experimental results demonstrate that our approach scales to up to 10,000 agents and significantly outperforms current methods across diverse dynamic constraints in large-scale MAPF settings.

Technology Category

• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Modern Multi-Agent Path Finding (MAPF) algorithms must plan for hundreds to thousands of agents in congested environments within a second, requiring highly efficient algorithms. Priority Inheritance with Backtracking (PIBT) is a popular algorithm capable of effectively planning in such situations. However, PIBT is constrained by its rule-based planning procedure and lacks generality because it restricts its search to paths that conflict with at most one other agent. This limitation also applies to Enhanced PIBT (EPIBT), a recent extension of PIBT. In this paper, we describe a new perspective on solving MAPF by planning over agent dependencies. Taking inspiration from PIBT's priority inheritance logic, we define the concept of agent dependencies and propose Multi-Dependency PIBT (MD-PIBT) that searches over agent dependencies. MD-PIBT is a general framework where specific parameterizations can reproduce PIBT and EPIBT. At the same time, alternative configurations yield novel planning strategies that are not expressible by PIBT or EPIBT. Our experiments demonstrate that MD-PIBT effectively plans for as many as 10,000 homogeneous agents under various kinodynamic constraints, including pebble motion, rotation motion, and differential drive robots with speed and acceleration limits. We perform thorough evaluations on different variants of MAPF and find that MD-PIBT is particularly effective in MAPF with large agents.

Problem

• Multi-Agent Path Finding
• agent dependencies
• PIBT
• scalability
• large agents

Innovation

• Multi-Agent Path Finding
• Agent Dependencies
• Priority Inheritance
• MD-PIBT
• Kinodynamic Constraints