Adaptive path planning method for multi-AUVs in complex environments

Abstract

In modern marine scientific exploration, environmental protection, and resource investigation missions, autonomous underwater vehicles (AUV) have seen increasingly widespread applications. However, complex underwater environments present challenges such as multi-objective optimization conflicts and communication constraints, particularly in scenarios with high-density obstacles and dynamic ocean current disturbances. This paper proposes a Distributed Learning-Enhanced Double Deep Q-Network (DLE-DDQN) algorithm for adaptive path planning, integrating deep reinforcement learning with distributed collaborative optimization. The algorithm employs an improved Double Deep Q-Network (DDQN) architecture and a dynamically adaptive reward function. These components guide AUVs in path planning within unknown environments. Meanwhile, distributed learning mechanisms are leveraged to enhance training efficiency under limited communication bandwidth. Experimental results demonstrate that DLE-DDQN achieves an 18.7% improvement in task success rate, a 29.6% increase in path efficiency, and a 51.4% reduction in response time compared to traditional DDQN and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) methods in high-obstacle-density scenarios. Through a cross-attention mechanism that fuses environmental perception with navigational states, the algorithm maintains path curvature stability with a standard deviation below 0.15 rad/m even under abrupt current disturbances. These results validate the algorithm’s robustness and real-time performance in unstructured dynamic environments, confirming its capability to overcome both static obstacles and current interference for rapid, precise path planning.