Research on the IMOACO Path Planning Algorithm for Rescue AUVs

To address the challenges faced by autonomous underwater vehicles (AUVs) in search and rescue missions—specifically, vulnerability to ocean current interference and low task efficiency in complex marine environments—this paper proposes an Improved Multi-objective Ant Colony Optimization (IMOACO) algorithm. By incorporating ocean current dynamics and energy constraints, a current-guided multi-objective evaluation function and state transition function are constructed to guide AUVs to preferentially follow downstream paths. On this basis, the entropy weight method is integrated to enhance the heuristic function and pheromone update strategy of the Ant Colony Optimization (ACO), and a dynamic priority strategy is employed to optimize the traversal sequence of multiple objectives. Grid-based simulations using real nautical charts and field trials with the “Xinghai 300R” AUV demonstrate that the proposed method significantly improves path smoothness and mission efficiency, with the IMOACO algorithm achieving a 34.7% increase in multi-objective search efficiency. The results indicate that this method is well-suited for multi-objective search and rescue missions in environments with strong ocean current disturbances, offering strong potential for practical engineering applications.