研究真实环境下多机器人路径规划的权衡，提升工业部署可行性。
Analyzing Planner Design Trade-offs for MAPF under ADG-based Realistic Execution
- 基于行动依赖图（ADG）构建真实执行环境下的评估框架
- 发现解的最优性与实际执行性能存在显著权衡
- 揭示模型精度与规划最优性之间的关键取舍关系
多智能体路径规划（MAPF）算法正被广泛应用于工业仓库和自动化制造场景，要求机器人在真实物理约束下可靠运行。然而，现有评估框架通常依赖简化机器人模型，导致算法基准与实际性能存在巨大差距。近期框架SMART结合动力学建模与基于行动依赖图（ADG）的执行机制，实现了大规模、真实的MAPF评估。本文在此基础上，系统研究三个核心设计因素对真实执行性能的影响：（1）解的最优性与执行表现的关系；（2）系统性能对动力学建模误差的敏感度；（3）模型精度与规划最优性之间的权衡。通过实证分析，揭示了这些设计选择在真实场景中的影响，指出开放挑战与未来研究方向，以推动社区向实际部署迈进。
原文摘要 · Abstract (English)
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.
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