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

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Abstract

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.

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Acknowledgments

The authors would like to thank the associate editor and anonymous reviewers for their helpful suggestions and reviews. This work is supported in part by the National Natural Science Foundation of China under Grants 61273352, 61175111, 60805038 and in part by the Open Foundation of the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences under Grants 20130101 and 20140107.

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Authors and Affiliations

  1. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Zhiqiang Cao, Long Cheng, Chao Zhou, Xu Wang & Min Tan

  2. Centre for Intelligent Systems Research, Deakin University, Waurn Ponds, VIC, Australia

    Nong Gu

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  1. Zhiqiang Cao
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  2. Long Cheng
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  3. Chao Zhou
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Correspondence to Zhiqiang Cao.

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Cao, Z., Cheng, L., Zhou, C. et al. Spiking neural network-based target tracking control for autonomous mobile robots. Neural Comput & Applic 26, 1839–1847 (2015). https://doi.org/10.1007/s00521-015-1848-5

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