Research on the Stability Motion Control Method of Underwater Vehicle Driven by Data

Abstract

Due to the continuous depletion of land resources, many countries have turned their attention to the oceans, which are rich in mineral resources and fishery resources. Intelligent underwater robots can autonomously implement the exploitation of marine resources in the underwater environment and avoid the safety risks caused by artificial underwater operations. The autonomous navigation capabilities of underwater robots are an important prerequisite for their successful underwater operations. Regarding the conventional autonomous navigation algorithm in the complex underwater environment, the amount of calculation is too large to realize real-time navigation, while the reinforcement learning will encounter dimensional disasters and require a lot of time to train the learning. This paper proposes to use an improved DDQN algorithm to study the autonomous navigation of underwater robots in unknown environments. This algorithm gives underwater robots the ability to learn autonomously and improves the robot’s self-adaptation in different environments. It solves the bottleneck problem of traditional autonomous navigation algorithms and realizes the autonomous navigation of underwater robots in an environment without map information.