Based on Deep Reinforcement Learning to path planning in uncertain ocean currents for Underwater Gliders

Abstract

This paper studies the application of Deep Reinforcement Learning (DRL) in the field of underwater glider (UG) formation path planning and solves the path planning of glider formation in uncertain ocean currents. Facing the uncertain ocean current environment, based on the observation and collection of ocean current data by the overall ocean observation system, the glider formation needs to have the ability of path planning. The original DRL algorithm network architecture is simple, and the feature extraction ability is limited. According to the characteristics of ocean current and path planning, a Deep Convolutional Neural Network for uncertain ocean current environment (Duo-CNN) network architecture with a dual-branch structure is proposed. Based on the DRL, the glider model and the interaction between the glider and the environment are used to guide the path planning through the implicit prediction of the known ocean current data. A solution for formation construction, obstacle avoidance, and path planning of glider formation in a dynamic uncertain ocean current environment is provided. The simulation verifies the effectiveness of the path planning of the glider formation using the DRL of the dual-branch Duo-CNN architecture. Compared with other architectures with feature extraction ability, Duo-CNN has advantages.