An Analysis of Constraint-Based Multi-Agent Pathfinding Algorithms

2025-11-23

AI Summary

This paper addresses the lack of systematic guidance for constraint design in Multi-Agent Path Finding (MAPF) and Multi-Robot Motion Planning (MRMP). We propose the first classification framework for constraints—distinguishing “conservative” and “aggressive” types—based on their strictness. Methodologically, we hierarchically integrate conflict constraints into Conflict-Based Search (CBS) and CBS with Priorities (CBSw/P), leveraging a hybrid grid–roadmap representation and conducting systematic experiments across multi-resolution and multi-density scenarios. Our key contributions are threefold: (i) the first quantitative characterization of the fundamental trade-off between solution rate and path quality—aggressive constraints improve feasibility in high-density/high-resolution settings, whereas conservative constraints preserve optimality of successful solutions; (ii) a reusable algorithm-selection decision flowchart; and (iii) generalization of these insights to MRMP. All code and experimental data are publicly available.

Technology Category

• Multiagent Systems: Multiagent Planning
• Constraint Satisfaction and Optimization: Search
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

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

Abstract

This study informs the design of future multi-agent pathfinding (MAPF) and multi-robot motion planning (MRMP) algorithms by guiding choices based on constraint classification for constraint-based search algorithms. We categorize constraints as conservative or aggressive and provide insights into their search behavior, focusing specifically on vanilla Conflict-Based Search (CBS) and Conflict-Based Search with Priorities (CBSw/P). Under a hybrid grid-roadmap representation with varying resolution, we observe that aggressive (priority constraint) formulations tend to solve more instances as agent count or resolution increases, whereas conservative (motion constraint) formulations yield stronger solution quality when both succeed. Findings are synthesized in a decision flowchart, aiding users in selecting suitable constraints. Recommendations extend to Multi-Robot Motion Planning (MRMP), emphasizing the importance of considering topological features alongside problem, solution, and representation features. A comprehensive exploration of the study, including raw data and map performance, is available in our public GitHub Repository: https://GitHub.com/hannahjmlee/constraint-mapf-analysis

Problem

• Analyzing constraint classification for multi-agent pathfinding algorithms
• Comparing conservative vs aggressive constraints in CBS variants
• Providing constraint selection guidance for MAPF and MRMP systems

Innovation

• Categorizes constraints as conservative or aggressive
• Analyzes CBS and CBSw/P under hybrid grid-roadmap
• Provides decision flowchart for constraint selection