Dynamic Target Hunting Under Autonomous Underwater Vehicle (AUV) Motion Planning Based on Improved Dynamic Window Approach (DWA)

A dynamic distributed target hunting method is proposed for the problem of distributed moving target hunting by multiple Autonomous Underwater Vehicles (AUVs). By integrating the improved Dynamic Window Approach (DWA) with the Rapidly-exploring Random Tree (RRT) algorithm and incorporating collision avoidance rules between AUVs into the evaluation system of the DWA, the collision avoidance rules are quantified, and corresponding evaluation functions are established. This allows for the selection of motion trajectories that comply with the collision avoidance rules from the predicted trajectory set, improving the obstacle avoidance capability during AUV motion planning and enhancing the reliability of the target hunting task. The introduction of a consistency algorithm maintains the consistency of the group task information and ensures that the hunting strategy can be adjusted promptly in the event of an AUV failure, allowing the target hunting task to continue. Polynomial regression algorithms are used to predict the moving target's trajectory. Based on a polygonal hunting formation, the hunting potential points are dynamically allocated, and, finally, each AUV executes distributed motion planning towards the hunting potential points to form the hunting formation. Simulation results show that the proposed method achieves efficient multi-AUV-distributed dynamic target hunting.