Mobile robots' modular navigation controller using spiking neural networks

ISCAS OpenIR

	Mobile robots' modular navigation controller using spiking neural networks
	Wang, Xiuqing (1); Hou, Zeng-Guang (2); Lv, Feng (1); Tan, Min (2); Wang, Yongji (3); Hou, Z.-G.(zengguang.hou@ia.ac.cn)
	2014
发表期刊	Neurocomputing
ISSN	9252312
卷号	134 页码:230-238
摘要	Autonomous navigation plays an important role in mobile robots. Artificial neural networks (ANNs) have been successfully used in nonlinear systems whose models are difficult to build. However, the third generation neural networks - Spiking neural networks (SNNs) - contain features that are more attractive than those of traditional neural networks (NNs). Because SNNs convey both temporal and spatial information, they are more suitable for mobile robots' controller design. In this paper, a modular navigation controller based on promising spiking neural networks for mobile robots is presented. The proposed behavior-based target-approaching navigation controller, in which the reactive architecture is used, is composed of three sub-controllers: the obstacle-avoidance SNN controller, the wall-following SNN controller and the goal-approaching controller. The proposed modular navigation controller does not require accurate mathematical models of the environment, and is suitable to unknown and unstructured environments. Simulation results show that the proposed transition conditions for sub-controllers are feasible. The navigation controller can control the mobile robot to reach a target successfully while avoiding obstacles and following the wall to get rid of the deadlock caused by local minimum. © 2014 Elsevier B.V.; Autonomous navigation plays an important role in mobile robots. Artificial neural networks (ANNs) have been successfully used in nonlinear systems whose models are difficult to build. However, the third generation neural networks - Spiking neural networks (SNNs) - contain features that are more attractive than those of traditional neural networks (NNs). Because SNNs convey both temporal and spatial information, they are more suitable for mobile robots' controller design. In this paper, a modular navigation controller based on promising spiking neural networks for mobile robots is presented. The proposed behavior-based target-approaching navigation controller, in which the reactive architecture is used, is composed of three sub-controllers: the obstacle-avoidance SNN controller, the wall-following SNN controller and the goal-approaching controller. The proposed modular navigation controller does not require accurate mathematical models of the environment, and is suitable to unknown and unstructured environments. Simulation results show that the proposed transition conditions for sub-controllers are feasible. The navigation controller can control the mobile robot to reach a target successfully while avoiding obstacles and following the wall to get rid of the deadlock caused by local minimum. © 2014 Elsevier B.V.
收录类别	SCI ; EI
关键词	Mobile Robot Spiking Neural Networks Modular Navigation Controller Target-approaching Obstacle-avoidance Wall-following
部门归属	(1) Hebei Normal University, Shijiazhuang 050031, China; (2) State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, The Chinese Academy of Sciences, Beijing 100190, China; (3) Laboratory for Internet Technologies, State Key Laboratory of Computer Science, Institute of Software, The Chinese Academy of Sciences, Beijing 100190, China
语种	英语
WOS记录号	WOS:000335486000030
引用统计
内容类型	期刊论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16856
专题	中国科学院软件研究所
通讯作者	Hou, Z.-G.(zengguang.hou@ia.ac.cn)
推荐引用方式 GB/T 7714	Wang, Xiuqing ,Hou, Zeng-Guang ,Lv, Feng ,et al. Mobile robots' modular navigation controller using spiking neural networks[J]. Neurocomputing,2014,134:230-238.
APA	Wang, Xiuqing ,Hou, Zeng-Guang ,Lv, Feng ,Tan, Min ,Wang, Yongji ,&Hou, Z.-G..(2014).Mobile robots' modular navigation controller using spiking neural networks.Neurocomputing,134,230-238.
MLA	Wang, Xiuqing ,et al."Mobile robots' modular navigation controller using spiking neural networks".Neurocomputing 134(2014):230-238.