Path Planning for Transoceanic Underwater Glider Based on Hybrid Reinforcement Learning Algorithm

Abstract

Underwater gliders (UGs) represent a class of autonomous underwater vehicles renowned for their extended operational endurance, capable of traversing thousands of kilometers. As the primary environmental factor influencing UG navigation, ocean currents profoundly affect path planning strategies, with temporal variability exhibiting significant regional disparities across marine domains. This study introduces a hybrid path-planning methodology for transoceanic UGs, synergizing classical algorithms with reinforcement learning techniques. The framework initiates by constructing a transoceanic current model through integration of multitemporal hydrodynamic data from the Global Ocean Physics Analysis and Forecast system. Subsequently, the Dijkstra algorithm generates initial trajectory planning within a static current field derived from long-term averaged flow patterns. Leveraging these preliminary waypoints, a Q-learning algorithm performs segmented trajectory optimization using real-time current data. The refined path undergoes final processing through a smoothing algorithm to yield navigable routes compatible with UG operational constraints. Accompanying this methodology, a dedicated software platform, UG path planning platform (UGPPP), facilitates path planning and performance evaluation. Validation through case studies in the South China Sea, Western Pacific, and transoceanic regions demonstrates the proposed method’s superior overall performance compared to benchmark approaches, achieving a 35.946% hydrodynamic energy utilization rate. This systematic approach establishes a foundational framework for optimizing long-range marine vehicle navigation in dynamic oceanic environments.