Multi-USV Coverage Path Planning Using Spatial Graph Multi-Actor-Attention-Critic Reinforcement Learning Framework With Operator Pooling

Yuanbo Zhu

doi:10.1109/TMC.2025.3599616

Multi-USV Coverage Path Planning Using Spatial Graph Multi-Actor-Attention-Critic Reinforcement Learning Framework With Operator Pooling

2025-08-15

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DOI：10.1109/TMC.2025.3599616

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Multi-uncrewed surface vehicle coverage path planning (MCPP) presents significant challenges in large-scale aquatic environments due to dynamic ocean currents, non-euclidean spatial structures, and task load imbalance. To address these challenges, we propose the spatial graph multi-agent actor-attention-critic (SGMAAC) framework, which integrates a novel spatial graph attention network (SpGAT) for adaptive non-euclidean feature extraction and operator pooling for dynamic path optimization and task load balance. Specifically, SpGAT captures global-local topological dependencies to enhance decision-making under irregular topography, while operator pooling employs multi-step grow, deduplicate, and exchange operations to eliminate redundant paths and balance task loads across USVs. Additionally, SGMAAC introduces a multi-objective reward function that jointly optimizes coverage efficiency, collision avoidance, and energy consumption, enabling coordinated path planning in large-scale aquatic environments. Experimental results demonstrate that SGMAAC outperforms baseline methods across diverse aquatic scenarios, achieving improvements in convergence speed, makespan, task load balance, and path costs.

Keywords:

Multi-uncrewed surface vehicle coverage path planning

non-euclidean spatial data

spatial graph multi-agent actor-attention-critic framework

spatial graph attention network

operator pooling

Multi-USV Coverage Path Planning Using Spatial Graph Multi-Actor-Attention-Critic Reinforcement Learning Framework With Operator Pooling

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