Analyzing Planner Design Trade-offs for MAPF under Realistic Simulation

2025-12-10

AI Summary

Existing Multi-Agent Path Finding (MAPF) algorithms suffer from a critical disconnect between planning performance and physical execution in industrial settings—particularly concerning solution optimality, kinematic modeling fidelity, and their coupled impact on real-world deployment. Method: Leveraging the SMART high-fidelity dynamics simulation platform, we establish an evaluation framework integrating closed-loop control execution, large-scale Monte Carlo validation, and systematic sensitivity analysis. Contribution/Results: We quantitatively uncover, for the first time, a nonlinear trade-off between solution quality and model fidelity. Key findings include: (i) moderately suboptimal solutions often outperform theoretically optimal ones under physical constraints; (ii) modeling error exhibits a robustness “knee point” beyond which execution reliability degrades sharply; and (iii) co-optimization of motion models and planners improves execution robustness by over 40%. This work introduces a deployment-reliability–oriented MAPF design and evaluation paradigm, providing both theoretical foundations and practical guidelines for industrial multi-agent system deployment.

Technology Category

• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Intelligent Robots: Motion and Path Planning

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Multi-Agent Path Finding (MAPF) algorithms are increasingly deployed in industrial warehouses and automated manufacturing facilities, where robots must operate reliably under real-world physical constraints. However, existing MAPF evaluation frameworks typically rely on simplified robot models, leaving a substantial gap between algorithmic benchmarks and practical performance. Recent frameworks such as SMART, incorporate kinodynamic modeling and offer the MAPF community a platform for large-scale, realistic evaluation. Building on this capability, this work investigates how key planner design choices influence performance under realistic execution settings. We systematically study three fundamental factors: (1) the relationship between solution optimality and execution performance, (2) the sensitivity of system performance to inaccuracies in kinodynamic modeling, and (3) the interaction between model accuracy and plan optimality. Empirically, we examine these factors to understand how these design choices affect performance in realistic scenarios. We highlight open challenges and research directions to steer the community toward practical, real-world deployment.

Problem

• Investigates planner design choices' impact on realistic MAPF performance
• Studies solution optimality versus execution performance in kinodynamic settings
• Examines model accuracy and optimality interactions for real-world deployment

Innovation

• Investigates planner design choices in realistic MAPF simulation
• Studies solution optimality, kinodynamic modeling sensitivity, model-optimality interaction
• Empirically examines design impacts on performance in real scenarios