{'fraction_non_alphanumeric': 0.023214285714285715, 'fraction_numerical': 0.0008928571428571428, 'mean_word_length': 5.193370165745856, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An obstacle avoiding method of autonomous underwater vehicle based on the reinforcement learning

Autonomous Underwater Vehicle (AUV) is referred as a promising device to extend human intelligences to harsh underwater environment. Most applications of AUV demand optimal tracking path, however, the marine environment is very complex, such as reefs, corals, trenches and even marine emergencies (especially gathering fish), so it is very important for AUV to avoid obstacles smoothly during exploring. In this paper, the obstacle avoidance of AUV is realized by means of reinforcement learning. The AUV is assumed to be auto navigated in surge, sway and yaw. First, this paper considers static obstacles and give the 3-DOF mathematical model of AUV. Then, For obstacle avoidance, establishing a new cost function is considered in reinforcement learning. This new cost function is based on the distance between the AUV and the obstacles, and combined with the total cost function to achieve the optimal tracking path of AUV. Finally, the simulation results are presented to prove the effectiveness of the proposed method.