A Hybrid A*D3QN Framework with Prior Knowledge and Multimodal Data Fusion for USV Path Planning

Path planning is an essential task for the mission execution of unmanned surface vehicle (USV). However, existing advanced techniques based on deep reinforcement learning (DRL) often suffer from low learning efficiency and insufficient environmental perception from single-sensor configurations. To address these issues, this paper proposes a hybrid framework named A*D3QN, which integrates the heuristic efficiency of the A* algorithm with the adaptive decision-making of a dueling double deep Q-network (D3QN), associated with the multimodal data fusion for precise environment modeling. The proposed A*D3QN incorporates prior knowledge from the global paths generated by A* to initialize and guide the D3QN learning process. The prior knowledge, formatted as RL transition tuples, is used in the reward function design and the N-step prioritized experience replay, which significantly accelerates overall learning efficiency. Moreover, an improved D3QN architecture is designed to dynamically fuse visual data and navigation states via a cross-entropy attention mechanism, enabling multimodal perception in partially unknown environments. Extensive experiments across three scenarios with varying obstacle densities demonstrate that A*D3QN significantly outperforms state-of-the-art DRL baselines. Ablation studies further validate the necessity of each component.