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.
From: Mingjia Chen [view email]
[v1]
Sun, 28 Sep 2025 07:34:43 UTC (3,566 KB)
[v2]
Sun, 23 Aug 2026 12:18:12 UTC (813 KB)
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