Alpha Evolution Algorithm based 3D Path Planning Leveraging Sea Environment Features

This paper presents an Alpha Evolution (AE) enhanced three-dimensional path planning approach for autonomous underwater vehicles (AUVs), leveraging sea current features and physical constraints. The proposed three-dimensional sea environment features framework adopts a hierarchical three-stage optimization architecture with novel components: 1) environmental-feature-driven reference point sampling, 2) the reference path generated based on the reference point, and 3) the final path generated by the optimization algorithm. Simulation results on various map scales show that 3D-SEF outperforms classical methods in robustness and convergence speed. The proposed approach is promising for applications in sea environmental monitoring, marine rescue, and large-scale sea exploration.