Coordinated Trajectory Planning for Multiple Autonomous Underwater Vehicles: A Parallel Grey Wolf Optimizer

Abstract

The utilization of unmanned systems has witnessed a steady surge in popularity owing to its tremendous potential for a wide range of applications. In particular, the coordination among multiple vehicle systems has been demonstrated to possess unparalleled efficacy in accomplishing intricate and diverse tasks. In light of this, the present paper delves into the coordinated path planning mission that is accomplished by collaborative efforts amongst multiple Autonomous Underwater Vehicles (AUVs). First, considering the potential threats, arrival time windows, space, and physical constraints for the AUVs, a sophisticated coordinated path planning model is formulated in a 3D environment, serving as a systematic and structured blueprint for the underlying mechanism. Subsequently, the optimization problem is addressed through the incorporation of a restricted initialization scheme and a multi-objective clustering strategy in the proposed methodology. The resulting approach leads to the development of the Parallel Grey Wolf Optimizer (P-GWO) which exhibits strong global searching abilities and a rapid convergence rate, rendering it a dependable and effective solution. The results demonstrate a 10–15% improvement in convergence rate and a reduction of over 60% in the average cost value compared to reliable references, thus presenting an effective solution for underwater missions with specific requirements.