Authors: Guosheng Yang, Zeng-Guang Hou, Zize Liang
Addresses: Institute of Advanced Control and Intelligent Information, Henan University, Kaifeng 475001, China. ' Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China. ' Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China
Abstract: Distributed visual navigation based on neural Q-learning for a mobile robot is studied in this paper. First, a general distributed structure based on the multiple processors for visual navigation is established according to the decomposition of the mobile robot visual navigation task. Second, in terms of the general distributed structure, the local environment description method based on the Peer Group Filtering (PGF) and fuzzy technology is put forward. Third, in each local environment description, a controller based on neural Q-learning is designed to guide the mobile robot navigation. In the last part of this paper, experimental simulations are done to test the effectiveness of the presented distributed algorithm, including the image segmentation, environment description and navigation policy.
Keywords: distributed visual navigation; image processing; mobile robots; robot navigation; Q-learning; simulation; vehicle autonomous systems; autonomous vehicles; neural networks; fuzzy logic; robot vision.
International Journal of Vehicle Autonomous Systems, 2006 Vol.4 No.2/3/4, pp.225 - 238
Available online: 28 Jan 2007 *Full-text access for editors Access for subscribers Purchase this article Comment on this article