CADENCE: Predicting Realized MAPF Execution Time Beyond Sum of Costs
Overview
This research paper introduces CADENCE, a method to predict the actual execution time of Multi-Agent Path Finding (MAPF) plans for robot teams. The authors address the limitation of standard metrics like Sum of Costs (SoC) which often fail to capture realistic performance. Using a hardware study with seven differential drive robots in a 7x7 workcell, they analyzed 480 trials across various scenarios. The study finds that while SoC is informative, incorporating 'primitive motion burden' (such as makespan, turns, and start-stop transitions) provides the strongest improvement, reducing prediction error by approximately 48.6% to 59.8% in Mean Absolute Error compared to SoC-only models. Interaction-aware coordination features offer smaller gains. The results suggest that much of the execution time gap is visible in the offline plan before robots begin moving.
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Reported quantity · plans: 120 other · reported trials
“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 scena”
Exact source · revision 1Source owner not reported
Reported quantity · trials: 480 other · reported trials
“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 scena”
Exact source · revision 1Source owner not reported
Reported quantity · workcell dimensions: 7 other · Basis not reported
“ion 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”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalCADENCE: Predicting Realized MAPF Execution Time Beyond Sum of Costs
This research paper introduces CADENCE, a method to predict the actual execution time of Multi-Agent Path Finding (MAPF) plans for robot teams. The authors address the limitation of standard metrics like Sum of Costs (SoC) which often fail to capture realistic performance. Using a hardware study with seven differential drive robots in a 7x7 workcell, they analyzed 480 trials across various scenarios. The study finds that while SoC is informative, incorporating 'primitive motion burden' (such as makespan, turns, and start-stop transitions) provides the strongest improvement, reducing prediction error by approximately 48.6% to 59.8% in Mean Absolute Error compared to SoC-only models. Interaction-aware coordination features offer smaller gains. The results suggest that much of the execution time gap is visible in the offline plan before robots begin moving.
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