Page 85 - Read Online
P. 85

Li et al. Intell Robot 2021;1(1):58-83  I http://dx.doi.org/10.20517/ir.2021.08       Page 80



                   Consum Electron 2012;59:3211–20.
               15.  Yang SX, Meng MQH. Real­time collision­free motion planning of a mobile robot using a neural dynamics­based approach. IEEE Trans
                   Neural Netw 2003;14:1541–52.
               16.  Zhu A, Yang SX. Path planning of multi­robot systems with cooperation. In: Proceedings 2003 IEEE International Symposium on Com­
                   putational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium;
                   2003 Jul 16­20; Kobe, Japan. vol. 2. IEEE; 2003. pp. 1028–33.
               17.  Pan L, Yang SX. An electronic nose network system for online monitoring of livestock farm odors. IEEE ASME Trans Mechatron
                   2009;14:371–76.
               18.  Martynenko AI, Yang SX. Biologically inspired neural computation for ginseng drying rate. Biosyst Eng 2006;95:385–96.
               19.  Hodgkin AL, Huxley AF. A quantitative description of membrane current and its application to conduction and excitation in nerve. J
                   Physiol 1952;117:500–544.
               20.  Cohen MA, Grossberg S. Absolute stability of global pattern formation and parallel memory storage by competitive neural networks.
                   IEEE Trans Syst Man Cybern B Cybern 1983;SMC­13:815–26.
               21.  Grossberg S. Nonlinear neural networks: Principles, mechanisms, and architectures. Neural Networks 1988;1:17–61.
               22.  Öĝmen H, Gagné S. Neural models for sustained and ON­OFF units of insect lamina. Biol Cybern 1990;63:51–60.
               23.  Öǧmen H, Gagné S. Neural network architectures for motion perception and elementary motion detection in the fly visual system. Neural
                   Networks 1990;3:487–505.
               24.  Yang SX, Hu E. Real­time path planning and tracking control using a neural dynamics based approach. IFAC Proceedings Volumes
                   2002;35:103–8.
               25.  Ni J, Wu L, Shi P, Yang SX. A dynamic bioinspired neural network based real­time path planning method for autonomous underwater
                   Vehicles. Comput Intel Neurosc 2017;2017:1–16.
               26.  Ni J, Yang X, Chen J, Yang SX. Dynamic bioinspired neural network for multi­robot formation control in unknown environments. Int J
                   Rob Autom 2015;30.
               27.  Oh H, Shirazi AR, Sun C, Jin Y. Bio­inspired self­organising multi­robot pattern formation: a review. Robot Auton Syst 2017;91:83–100.
               28.  Yang SX, Zhu A, Meng MQH. Biologically inspired tracking control of mobile robots with bounded accelerations. In: IEEE International
                   Conference on Robotics and Automation, 2004. Proceedings. ICRA '04; 2004 Apr 26 ­May 1; New Orleans,USA. IEEE; 2004. pp. 1610–
                   15.
               29.  Yang SX, Meng M. An efficient neural network approach to dynamic robot motion planning. Neural Networks 2000;13:143–48.
               30.  Yang SX, Luo C. Neural dynamics and computation for navigation of multiple robots. In: IEEE International Conference on Systems,
                   Man and Cybernetics; 2002 Oct 6­9 ; Yasmine Hammamet, Tunisia. IEEE; 2002. pp. 515–20.
               31.  Yang SX, Meng M, Li H. A neural computation model for real­time collision­free robot navigation. IFAC Proceedings Volumes
                   2002;35:323–28.
               32.  Yang X, Meng M. An efficient neural network model for path planning of car­like robots in dynamic environment. In: Engineering
                   Solutions for the Next Millennium. 1999 IEEE Canadian Conference on Electrical and Computer Engineering (Cat. No.99TH8411);1999
                   May 9­12; Edmonton,Canada. IEEE; 1999. pp. 1374–79.
               33.  Yang SX, Meng M, Yuan X. A biological inspired neural network approach to real­time collision­free motion planning of a nonholo­
                   nomic car­like robot. In: Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat.
                   No.00CH37113); 2000 Oct 31­Nov 5; Takamatsu, Japan. IEEE; 2000. pp. 239–44.
               34.  Yuan X, Yang SX. Virtual assembly with biologically inspired intelligence. IEEE Trans Syst Man Cybern, Part C(Appl rev) 2003;33:159–
                   67.
               35.  Luo M, Hou X, Yang SX. A multi­scale map method based on bioinspired neural network algorithm for robot path planning. IEEE
                   Access 2019;7:142682–91.
               36.  Ni J, Li X, Hua M, Yang SX. Bioinspired neural network­based Q­learning approach for robot path planning in unknown environments.
                   Int J Rob Autom 2016;31:464–74.
               37.  Ni J, Li X, Fan X, Shen J. A dynamic risk level based bioinspired neural network approach for robot path planning. In: 2014 World
                   Automation Congress (WAC); 2014 Aug 3­7; Waikoloa, USA. IEEE; 2014. pp. 829–33.
               38.  Chen Y, Xu W, Li Z, et al. Safety­enhanced motion planning for flexible surgical manipulator using neural dynamics.
                   IEEE Trans Control Syst Technol 2017;25:1711–23.
               39.  Yang X, Meng M. A neural network approach to real­time path planning with safety consideration. In: SMC'98 Conference Proceedings.
                   1998 IEEE International Conference on Systems, Man, and Cybernetics; 1998 Oct 14­14; San Diego, USA. IEEE; 1998. pp. 3412–17.
               40.  Yang SX, Meng M. An efficient neural network method for real­time motion planning with safety consideration. Robot Auton Syst
                   2000;32:115–28.
               41.  Glasius R, Komoda A, Gielen SCAM. Neural network dynamics for path planning and obstacle avoidance. Neural Networks 1995;8:125–
                   33. [DOI: 10.1016/0893­6080(94)e0045­m]
   80   81   82   83   84   85   86   87   88   89   90