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arXiv · 2509.23705

MDCPP: Multi-Robot Dynamic Coverage Path Planning for Workload Adaptation

Jun Chen·Mingjia Chen·Tianlong Yu·Qi Nie·Shinkyu Park
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Abstract

Multi-robot coverage path planning commonly balances geometric area or path length under a constantspeed assumption. This assumption is inadequate when sensing or interaction tasks cause spatially varying traversal speeds, because equal areas can induce markedly different completion times. We propose Multi-Robot Dynamic Coverage Path Planning (MDCPP), which learns a Gaussian-mixture workload field from partial observations, predicts cell-wise service times, and repeatedly repartitions the uncovered cells through a distributed capacity-constrained assignment. We establish finite termination and pairwise local optimality of each synchronized assignment round, bound the service-time makespan degradation due to estimation error, and state sufficient conditions for complete coverage. A 600-run benchmark against sweeping, LS-MCPP, reactive reassignment, and an oracle shows that prediction is most valuable under strong heterogeneity and improves aggregate paired makespan over the nonpredictive alternatives. A three-UGV experiment further validates route execution and spatial speed adaptation under localization, drivetrain, and wireless-control effects.

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Jun Chen, Mingjia Chen, Tianlong Yu, Qi Nie, Shinkyu Park. 2026-08-23. MDCPP: Multi-Robot Dynamic Coverage Path Planning for Workload Adaptation. https://arxiv.org/abs/2509.23705

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