An LLM-Assisted AUV 3-D Path Planning Scheme Under Ocean Current Interference via Reinforcement Learning

With the rapid development of Industrial Internet of Things (IIoT), the emergence of credible federated learning provides a more effective solution for it. In this article, we use the credible collaboration between large language models (LLMs) and reinforcement learning (RL) model to improve the autonomous decision-making efficiency of autonomous underwater vehicle (AUV), reduce resource and power consumption, and solve robust decision-making problem in open environments. First, considering the complex terrain and hydrodynamic environment in the ocean, we construct a 3-D ocean simulation environment with high accuracy and high reliability to simulate the behavioral constraints of AUV in the real ocean. Second, we integrate LLaMA model into the decision-making process of AUV, utilizing its powerful information processing capability for environmental analysis and action selection, so as to improve the decision-making generalization ability of AUV in dynamic ocean environments. Finally, we propose proximal policy advantage estimation (PPAE) method and achieve safe and efficient path planning for AUV based on LLMs decision output and dynamic field environment information. The experimental results show that our method achieves a good effect in improving the decision accuracy and robustness of the AUV, which proves the effectiveness of the LLMs in the application of underwater intelligent agent control decision.