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| a target-reaching controller for mobile robots using spiking neural networks | |
| Wang Xiuqing; Hou Zeng-Guang; Lv Feng; Tan Min; Wang Yongji | |
| 2012 | |
| Conference Name | 19th International Conference on Neural Information Processing, ICONIP 2012 |
| Source | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 652-659 |
| Conference Date | November 12, 2012 - November 15, 2012 |
| Conference Place | Doha, Qatar |
| Indexed Type | EI |
| ISSN | 0302-9743 |
| ISBN | 9783642344770 |
| Department | (1) Vocational and Technical Institute Hebei Normal University Shijiazhuang Hebei 050031 China; (2) State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100090 China; (3) State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China |
| English Abstract | Autonomous navigation plays important role in mobile robots. In this paper, a navigation controller based on spiking neural networks (SNNs) for mobile robots is presented. The proposed target-reaching navigation controller, in which the reactive architecture is used, is composed of three sub-controllers: the obstacle-avoidance controller and the wall-following controller using spiking neural networks (SNNs), and the goal-reaching controller. The experimental results show that the navigation controller can control the mobile robot to reach the target successfully while avoiding the obstacle and following the wall to get rid of the deadlock caused by local minimum. The proposed navigation controller does not require accurate mathematical models of the environment, and is suitable to unknown and unstructured environment. © 2012 Springer-Verlag.; Autonomous navigation plays important role in mobile robots. In this paper, a navigation controller based on spiking neural networks (SNNs) for mobile robots is presented. The proposed target-reaching navigation controller, in which the reactive architecture is used, is composed of three sub-controllers: the obstacle-avoidance controller and the wall-following controller using spiking neural networks (SNNs), and the goal-reaching controller. The experimental results show that the navigation controller can control the mobile robot to reach the target successfully while avoiding the obstacle and following the wall to get rid of the deadlock caused by local minimum. The proposed navigation controller does not require accurate mathematical models of the environment, and is suitable to unknown and unstructured environment. © 2012 Springer-Verlag. |
| Keyword | Collision Avoidance Data Processing Human Computer Interaction Mathematical Models Navigation Neural Networks |
| Sponsorship | United Development Company PSC (UDC); Qatar Petrochemical Company; ExxonMobil; Qatar Petroleum; Texas A and M University at Qatar; Asia Pacific Neural Network Assembly |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/15840 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Wang Xiuqing,Hou Zeng-Guang,Lv Feng,et al. a target-reaching controller for mobile robots using spiking neural networks[C],2012:652-659. |
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