Analyzing Planner Design Trade-offs for MAPF under Realistic Simulation

TL;DR

This work addresses the gap between abstract MAPF planning and real-world robotics by leveraging the SMART simulation framework to study how planner design choices influence execution under kinodynamic constraints. It systematically evaluates (i) plan optimality, (ii) kinodynamic-model accuracy, and (iii) the trade-off between model fidelity and plan quality, using execution-aware metrics like Average Execution Time (AET). The findings show that more accurate MAPF models (e.g., including rotations or full kinodynamics) generally improve execution performance, but at the cost of scalability, and that SoC is only a partial predictor of real-world performance. The paper highlights practical directions for deployment, including execution-aware objectives, scalable kinodynamic planning, adaptive trade-offs, and enhanced execution frameworks.

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.

Figures (4)

• Figure 1: Relationship between SoC and AET. Each dot represents the SoC and AET of a single MAPF plan.
• Figure 2: AET using different MAPF models.
• Figure 3: Paths generated on different maps.
• Figure 4: AET using different MAPF models and planners.