D-ACO-Based Path Planning for AUV in UWSN: A Hybrid Approach Combining DQN and ACO

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Abstract
In an underwater wireless sensor network (UWSN), autonomous underwater vehicle (AUV) path planning faces challenges of high energy consumption, path instability, and limited adaptability in dynamic 3-D underwater environments. To overcome these limitations, a hybrid deep Q-network and ant colony optimization (D-ACO) algorithm is proposed, integrating the adaptive learning of deep Q-network (DQN) with the global search ability of ant colony optimization (ACO) to enhance stability and multiobjective optimization. In particular, a multiobjective reward mechanism is designed to jointly optimize energy consumption, task timeliness, and data acquisition efficiency, enabling the AUV to achieve effective obstacle avoidance while minimizing redundant movements. To validate its effectiveness, the D-ACO, ACO, DQN, PSO, GWO, and artificial potential field (APF) algorithms are applied in a 3-D grid environment for comparative analysis, focusing on coverage, task execution time, energy consumption, and path stability. Simulation results demonstrate that the D-ACO algorithm exhibits superior performance in balancing multiple objectives, achieving better convergence. This study delivers an efficient solution for underwater data acquisition and provides new insights into applying deep reinforcement learning (DRL) to autonomous system path planning.

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