Multi-Agent Path Finding (MAPF) algorithms are increasingly used to plan motion for robot teams in industrial warehouses and robotic shared workspaces, but standard MAPF algorithm evaluation metrics, such as Sum of Costs (SoC), makespan, and planner runtime, can obscure how planner choices translate into realistic execution performance. We present CADENCE (Coordination and Action-Driven Estimation for Networked Continuous Execution), a hardware study of this evaluation gap on a fixed 7 by 7 workcell with seven differential drive robots, asking which features available before execution can best predict final wall-clock completion time. We compare SoC, total planned travel cost, primitive motion burden (how much basic motion the plan requires, such as makespan, turns, consecutive moves, and start-stop transitions), and interaction aware coordination structure (how much inter-robot coordination the plan induces, such as dependency links, interacting robot pairs, dependency depth, and crowding exposure). To test this, we generate 120 plans across 15 scenarios -- 5 Empty, 5 Medium Random, and 5 Bottleneck and execute each plan four times, yielding a 480 trial hardware corpus. Using both a scenario-held -- out ridge model and a trial-level mixed-effects model, we find that SoC alone is informative but incomplete, while primitive motion burden gives the strongest improvement, reducing held out error by about 48.6%-59.8% in MAE and 44.2%-61.4% in RMSE relative to SoC-only models. Interaction-aware coordination features add smaller, less uniform gains, most clearly in the mixed-effects analysis. Across both models and uncertainty checks, primitive motion burden is the most reliable additional signal beyond SoC, suggesting that much of the execution time gap is already visible in the offline plan before any robot starts moving.

翻译：中文摘要：多智能体路径规划（MAPF）算法日益广泛地应用于工业仓库与机器人共享工作空间中的机器人编队运动规划，但标准MAPF算法评估指标（如总代价和、完工时间及规划器运行时间）可能掩盖规划选择如何转化为实际执行性能。我们提出CADENCE（面向网络化连续执行的协作与动作驱动估计）框架，通过一个固定7×7工作单元中七台差分驱动机器人的硬件实验，研究这一评估差距，探究在执行前可获取的哪些特征最能预测最终实际时钟完成时间。我们比较了总代价和、规划行进总代价、基础动作负担（规划所需的基础运动量，如完工时间、转弯次数、连续移动及启停转换）以及交互感知协调结构（规划引发的机器人间协调程度，如依赖链接、交互机器人对、依赖深度及拥挤暴露度）。为此，我们在15个场景（5个空场景、5个中等随机场景、5个瓶颈场景）中生成120个规划，并对每个规划执行四次，构建包含480次试验的硬件语料库。通过场景留出岭回归模型和试验级混合效应模型，我们发现：仅依赖总代价和虽具有信息量但不够完整，而基础动作负担提供了最强的改进效果——与仅用总代价和的模型相比，其留出误差在MAE上降低约48.6%-59.8%，在RMSE上降低约44.2%-61.4%。交互感知协调特征带来的增益较小且非均匀，在混合效应分析中表现最为显著。在两类模型及不确定性检验中，基础动作负担是超越总代价和的最可靠附加信号，表明大部分执行时间差距在机器人开始移动前即已隐含于离线规划中。