Analyzing Planner Design Trade-offs for MAPF under ADG-based Realistic Execution

TL;DR

This paper investigates how different planner design choices impact the performance of MAPF algorithms under realistic, ADG-based robot execution models, highlighting trade-offs between optimality, model accuracy, and robustness.

Contribution

It systematically analyzes the effects of solution optimality, model inaccuracies, and model complexity on MAPF performance in realistic settings, guiding practical deployment.

Findings

Optimal solutions may not always yield best real-world performance.

Inaccuracies in kinodynamic modeling can significantly affect system robustness.

There is a tradeoff between model complexity and plan optimality.

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 combine kinodynamic modeling with execution based on the Action Dependency Graph (ADG), enabling realistic, large-scale MAPF 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 tradeoff 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.