Spiking neural network-based target tracking control for autonomous mobile robots

Cao, Zhiqiang, Cheng, Long, Zhou, Chao, Gu, Nong, Wang, Xu and Tan, Min 2015, Spiking neural network-based target tracking control for autonomous mobile robots, Neural computing and applications, vol. 26, no. 8, pp. 1839-1847, doi: 10.1007/s00521-015-1848-5.

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Title Spiking neural network-based target tracking control for autonomous mobile robots
Author(s) Cao, Zhiqiang
Cheng, Long
Zhou, Chao
Gu, Nong
Wang, Xu
Tan, Min
Journal name Neural computing and applications
Volume number 26
Issue number 8
Start page 1839
End page 1847
Total pages 9
Publisher Springer
Place of publication Berlin, Germany
Publication date 2015-11
ISSN 0941-0643
Keyword(s) Spiking neural network
Autonomous mobile robot
Spike trains
Target tracking
Hebbian learning
Science & Technology
Computer Science, Artificial Intelligence
Computer Science
Summary In this paper, a target tracking controller based on spiking neural network is proposed for autonomous robots. This controller encodes the preprocessed environmental and target information provided by CCD cameras, encoders and ultrasonic sensors into spike trains, which are integrated by a three-layer spiking neural network (SNN). The outputs of SNN are generated based on the competition between the forward/backward neuron pair corresponding to each motor, with the weights evolved by the Hebbian learning. The application to target tracking of a mobile robot in unknown environment verifies the validity of the proposed controller.
Language eng
DOI 10.1007/s00521-015-1848-5
Field of Research 080104 Computer Vision
0801 Artificial Intelligence And Image Processing
1702 Cognitive Science
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30075536

Document type: Journal Article
Collections: Centre for Intelligent Systems Research
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