caa a22 4500 606160108 CHVBK 20210128100628.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s00521-015-1848-5 doi (NATIONALLICENCE)springer-10.1007/s00521-015-1848-5 Spiking neural network-based target tracking control for autonomous mobile robots [Elektronische Daten] [Zhiqiang Cao, Long Cheng, Chao Zhou, Nong Gu, Xu Wang, Min Tan] 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. The Natural Computing Applications Forum, 2015 Spiking neural network nationallicence Autonomous mobile robot nationallicence Spike trains nationallicence Target tracking nationallicence Hebbian learning nationallicence Cao Zhiqiang State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China aut Cheng Long State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China aut Zhou Chao State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China aut Gu Nong Centre for Intelligent Systems Research, Deakin University, Waurn Ponds, VIC, Australia aut Wang Xu State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China aut Tan Min State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China aut Neural Computing and Applications Springer London 26/8(2015-11-01), 1839-1847 0941-0643 26:8<1839 2015 26 521 https://doi.org/10.1007/s00521-015-1848-5 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-015-1848-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Cao Zhiqiang State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China aut NATIONALLICENCE 700 1- Cheng Long State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China aut NATIONALLICENCE 700 1- Zhou Chao State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China aut NATIONALLICENCE 700 1- Gu Nong Centre for Intelligent Systems Research, Deakin University, Waurn Ponds, VIC, Australia aut NATIONALLICENCE 700 1- Wang Xu State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China aut NATIONALLICENCE 700 1- Tan Min State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/8(2015-11-01), 1839-1847 0941-0643 26:8<1839 2015 26 521