Path Planning Design for Autonomous Underwater Vehicles in 3D Environments Avoiding Static Obstacles

Efficient path planning is a major challenge in the field of autonomous underwater vehicles (AUVs), particularly in complex three-dimensional (3D) environments with static obstacles. This paper presents a novel approach for path planning design of these systems that utilizing the modified Grey Wolf Optimization (GWO) algorithm to enable AUVs to navigate through intricate underwater landscapes while avoiding stationary obstacles. To represent obstacles in the underwater environment, the Spherical Method is employed which transforms these impediments into 3D models within the simulation space, relying on their central coordinates. The factors such as the faster path planning time, path safety, and angle change are considered in order to find an appropriate path. Through the utilization of 3D environmental data and obstacle detection techniques, the algorithm facilitates real-time decision-making for the AUV, ensuring dynamic adaptation to the changing underwater landscape. Simulation results will be provided to validate the superior performance of the GWO-based path planning method compared to conventional approaches. Also, the algorithm's effectiveness is demonstrated in simulated 3D environments with diverse obstacle configurations.