Title :
Local Planning of AUV Based on Fuzzy-Q Learning in Strong Sea Flow Field
Author :
Yang, Ge ; Zhang, Rubo ; Xu, Dong ; Zhang, Ziyin
Author_Institution :
Sch. of Comput. Sci. & Technol., Harbin Eng. Univ., Harbin, China
Abstract :
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.
Keywords :
collision avoidance; fuzzy logic; fuzzy set theory; learning (artificial intelligence); mobile robots; underwater vehicles; AUV local planning; autonomous underwater vehicles; collision avoiding behavior; fuzzy behavior; fuzzy-Q learning; integrated reinforcement learning; logic method; moving-to-goal behavior; sea flow field; Algorithm design and analysis; Computer science; Fuzzy logic; Learning; Navigation; Optimization methods; Orbital robotics; Resists; Technology planning; Vehicle dynamics; AUV; Fuzzy-Q; local planning; see flow;
Conference_Titel :
Computational Sciences and Optimization, 2009. CSO 2009. International Joint Conference on
Conference_Location :
Sanya, Hainan
Print_ISBN :
978-0-7695-3605-7
DOI :
10.1109/CSO.2009.244
