Autonomous Reinforcement Control of Underwater Vehicles based on Monocular Depth Vision

Abstract In this paper, a monocular depth prediction based end-to-end reinforcement control framework is proposed for autonomous control of underwater vehicles in the unknown environment. In the control framework, with the input of camera sensor RGB videos, a monocular depth prediction network is proposed to generate underwater depth images and a sequential reinforcement learning controller is also developed for autonomous obstacle-avoiding navigation and movement control. Simulated and experimental results demonstrate that the proposed control scheme can achieve remarkable performance on collision-avoidance navigation and autonomous control in the unknown environment.