Integrated path planning for AUV communication efficiency and obstacle avoidance based on ant colony optimization and three-dimensional dynamic window algorithm

In tasks such as deep-sea exploration, underwater resource surveying, and archaeological excavation, autonomous underwater vehicles (AUVs) significantly improve the success rate of missions while lowering costs and safety risks. However, AUVs encounter challenges such as poor communication environments, low acoustic communication rates, and dynamic obstacle avoidance during underwater operations. To tackle these issues, this study proposes a multiobjective path planning approach that involves combining improved ant colony optimization (ACO) with a three-dimensional (3D) dynamic window approach (DWA) to optimize the global path planning of AUVs while avoiding obstacles and minimizing path length and communication propagation loss. By introducing nondominated sorting, enhanced heuristic functions, and pheromone update rules, the improved 3D ACO algorithm successfully solves multiobjective optimization problems and balances path length and propagation loss. Moreover, by incorporating the improved 3D-DWA method, the AUV can dynamically avoid obstacles in real time based on the global route supplied by the 3D ACO, guaranteeing safe navigation in dynamic marine environments. By allocating diverse weights to the pitch velocity regions in the DWA method, AUVs can respond to dynamic obstacles in the environment more flexibly, further increasing mission efficiency and safety. Experimental results show that compared with the traditional methods, this method demonstrates significant benefits in terms of path length and communication efficiency, stressing its potential application in complex marine environments.