Research on dynamic path planning of UUV based on improved ant colony algorithm and dynamic collision avoidance strategy

Traditional ant colony algorithms often fall into local optima and deadlocks, leading to unsolvable or suboptimal solutions in the path planning of unmanned underwater vehicles (UUVs). Furthermore, they lack the ability to dynamically avoid collisions. To address these issues, this paper proposes a dynamic path planning method that combines global path planning using an improved ant colony algorithm with a local dynamic collision avoidance strategy. First, the traditional ant colony algorithm is optimized to prevent local optima and deadlocks by employing a roulette wheel selection method and enhancing memory-based backtracking strategies. Subsequently, a Kalman filter-based trajectory prediction method is introduced to handle irregularly moving obstacles, along with specific collision avoidance strategies for different types of collisions. The proposed method ultimately achieves dynamic path planning for UUVs. Simulation experiments demonstrate that the improved ant colony algorithm achieves a convergence speed approximately 16.7% faster than the traditional algorithm and can effectively perform path planning in dynamic environments, resulting in an optimal collision-free path.