Local Planning of AUV Based on Fuzzy-Q learning in Strong Sea Flow Field

摘要：

This article integrated reinforcement learning with fuzzy logic method for AUV local planning under the strong sea flow field. A fuzzy behavior is defined to resist the sea flow by giving a extra angle towards sea flow. And Q-learning is used to adjust the peak point of fuzzy membership function of the resisting sea flow behavior. This behavior is complemented by two other behaviors, the moving-to-goal behavior and collision avoiding behavior. The recommendations of these three behaviors are integrated through adjustable weighting factors to generate the final motion command for the AUV. Simulation shows it improve the adaptability of AUV under different sea flow greatly.

作者: Ge Yang Rubo Zhang Dong Xu Ziyin Zhang

作者单位: School of Computer Science and Technology, Harbin Engineering University

会议类型: 国际会议

会议名称: The Second International Joint Conference on Computational Science and Optimization(CSO 2009)(2009 国际计算科学与优化会议)

会议地点: 三亚

会议语种:英文

页码: 994-998

在线出版日期: 2009-04-24（万方平台首次上网日期，不代表论文的发表时间）