A Multi-Dimensional Optimization Framework for AUV Cooperative Coverage Path Planning in Dynamic Underwater Environments

Efficient and robust coverage path planning for autonomous underwater vehicles (AUVs) is essential for applications such as ocean exploration, environmental monitoring, and military surveillance. This paper proposes a multi-dimensional optimization (MDO) framework for AUV cooperative coverage path planning, which improves coverage efficiency through the joint optimization of communication, path planning, and obstacle avoidance. First, a dynamic communication topology model is developed to adjust the communication radius of AUVs based on the number of neighboring vehicles. This model prevents network congestion and ensures real-time support for global path planning. Second, building on this communication model, an improved whale optimization algorithm is developed to determine optimal waypoints for area coverage. The algorithm integrates a convergence factor, a continuous optimization mechanism, and a random search strategy, thus enhancing path coverage uniformity and reducing energy consumption. Finally, to ensure safe navigation, the MDO framework incorporates an adaptive Tangent Bug algorithm for collision avoidance. This algorithm dynamically adjusts the detection range and heuristic distance to generate a smoother and safer collision avoidance path. Simulation results demonstrate that the MDO framework significantly improves coverage rate, energy efficiency, and obstacle avoidance performance, offering an effective solution for cooperative coverage path planning in dynamic underwater environments.