Path Planning for Autonomous Underwater Vehicles Based on an Improved Artificial Jellyfish Search Algorithm in Multi-Obstacle Ocean Current Environment

Path planning for autonomous underwater vehicles (AUVs) is a key research focus in the marine domain, requiring consideration of the underwater environment’s complexity and the efficiency of the planning algorithms. Firstly, a variety of strategies such as the memory function are integrated into the artificial jellyfish search algorithm (JS) to improve its convergence accuracy, and the improved artificial jellyfish search algorithm (IJS) is obtained. Secondly, this paper establishes a good objective function including the ocean current disturbance model, which helps the IJS algorithm better plan the paths to avoid obstacles and strong side currents. Furthermore, the optimal smoothed paths are obtained by using a cubic spline midpoint interpolation method. Finally, multiple simulation experiments are performed on the multi-obstacle ocean current model with realistic terrain data. The comparison results show that the IJS algorithm with a short running time has the optimal time cost and ocean current penalty cost for the planned path. In addition, the IJS algorithm is also shown to be adaptable in the field of multi-AUV movements.