Research on PID Motion Control System of Underwater Robot Based on Q-Learning

This paper proposes a reinforcement learning based motion control system for ROV underwater robots. In the past, the control system of ROV underwater robots often relied on pre-set path planning and control algorithms, which limited the performance and application scope of underwater robots, making it difficult for ROVs to cope with complex and changing underwater environments. To address this issue, this paper introduces reinforcement learning algorithms to achieve autonomous learning and adaptive control through the interaction learning between robots and their environment. Specifically, this article uses deep reinforcement learning algorithms to train underwater robots, enabling them to autonomously adjust their motion control strategies based on changes in the environment and task requirements. The experimental results show that the reinforcement learning based underwater robot motion control system exhibits better adaptability and robustness in complex underwater environments, with high control accuracy and efficiency.