Analyzing Planner Design Trade-offs for MAPF under Realistic Simulation
It addresses the gap between algorithmic benchmarks and practical performance for MAPF in industrial settings like warehouses, but is incremental as it builds on existing simulation frameworks.
This work investigates how key planner design choices, such as solution optimality and kinodynamic modeling accuracy, influence performance in Multi-Agent Path Finding (MAPF) under realistic simulation, highlighting open challenges for real-world deployment.
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.