AUV Collision Avoidance Strategy based on Fuzzy Reinforcement Learning

Collision avoidance is a key technology for autonomous underwater vehicle (AUV) to achieve tasks such as path planning, target searching, and map construction. The performance of the algorithm directly affects the safety of the AUV and the success of collision avoidance. To improve the algorithm's generalization and adaptability, a fuzzy reinforcement learning collision avoidance strategy is presented in this study. Firstly, reinforcement learning learns without explicit modeling and converge quickly, making it suitable for continuous online learning tasks. Therefore, this paper adopts a multi-step temporal difference reinforcement learning approach to control the collision avoidance of AUV. Then, fuzzy theory is integrated into reinforcement learning to address generalization issues in the state space. This integration allows the acquisition of continuous state inputs and action outputs, enhancing motion state recognition and improving operational smoothness. Finally, enhancing online training of the fuzzy reinforcement learning method through adaptive strategy adjustments has improved the algorithm's online optimization efficiency. The proposed AUV collision avoidance strategy is validated through simulations and experiments in a three-dimensional underwater environment. The research results demonstrate that this strategy can safety and efficiently guide the AUV, showcasing strong generalization capabilities.