Page 87 - Read Online
P. 87

Li et al. Intell Robot 2021;1(1):58-83  I http://dx.doi.org/10.20517/ir.2021.08       Page 82



                   tional Conference on Robotics and Biomimetics (ROBIO);2019 Dec 6­8; Dali, China. IEEE; 2019. pp. 1803–8.
               68.  Yu Z, Tao J, Xiong J, Yang SX. Design and analysis of path planning for robotic fish based on neural dynamics model. Int J Rob Autom
                   2021;36.
               69.  Yan M, Zhu D, Yang SX. A novel 3­D bio­inspired neural network model for the path planning of an AUV in underwater environments.
                   Intelligent Automation Soft Computing 2013;19:555–66.
               70.  Zhu D, Yang SX. Bio­inspired neural network­based optimal path planning for UUVs under the effect of ocean currents. IEEE Trans
                   Veh Technol 2021:1–1.
               71.  Zhu D, Li W, Yan M, Yang SX. The path planning of AUV based on D­S information fusion map building and bio­inspired neural
                   network in unknown dynamic environment. Int J Adv Robot Syst 2014;11:34.
               72.  Cao X, Peng J. A potential field bio­inspired neural network control algorithm for AUV path planning. In: 2018 IEEE In­
                   ternational Conference on Information and Automation (ICIA); 2018 Aug 11­13; Fujian, China. IEEE; 2018. pp. 1427–32.
               73.  Cao X, Chen L, Guo L, Han W. AUV global security path planning based on a potential field bio­Inspired neural network in underwater
                   environment. Intelligent Automation & Soft Computing 2021;27:391–407.
               74.  Zhu A, Yang SX. A neural network approach to dynamic task assignment of multirobots. IEEE Trans Neural Netw 2006;17:1278–87.
               75.  Zhu A, Yang SX. An improved SOM­based approach to dynamic task assignment of multi­robots. In: 2010 8th World Congress on
                   Intelligent Control and Automation; 2010 Jul 7­9; Jinan, China. IEEE; 2010. pp. 2168–73.
               76.  Yi X, Zhu A, Yang SX, Luo C. A bio­inspired approach to task assignment of swarm robots in 3­D dynamic environments. IEEE Trans
                   Cybern 2017;47:974–83.
               77.  Zhu D, Huang H, Yang SX. Dynamic task assignment and path planning of multi­AUV system based on animproved self­
                   organizing map and velocity synthesis method in three­dimensional underwater workspace. IEEE Trans Cybern 2013;43:504–14.
               78.  Huang H, Zhu D, Yuan F. Dynamic task assignment and path planning for multi­AUV system in 2D variable ocean current environment.
                   In: 2012 24th Chinese Control and Decision Conference (CCDC); 2012 May 23­25; Taiyuan, China. IEEE; 2012. pp. 999–012.
               79.  Zhu D, Cao X, Sun B, Luo C. Biologically inspired self­organizing map applied to task assignment and path planning of an AUV system.
                   IEEE Trans Cogn Commun Netw 2018;10:304–13.
               80.  Cao X, Zhu D. Multi­AUV task assignment and path planning with ocean current based on biological inspired self­organizing map and
                   velocity synthesis algorithm. Intelligent Automation & Soft Computing 2015;23:31–39.
               81.  Zhu D, Zhou B, Yang SX. A novel algorithm of multi­AUVs task assignment and path planning based on biologically inspired neural
                   network map. IEEE Trans Hum Mach Syst 2021;6:333–42.
               82.  Rui Z, Zhu D. Cooperative search algorithm For AUVs based on bio­inspired model. In: The 26th Chinese Control and Decision
                   Conference (2014 CCDC); 2014 May 31­Jun 2; Changsha, China. IEEE; 2014. pp. 4569–74.
               83.  Cao X, Zhu D, Yang SX. Multi­AUV target search based on bioinspired neurodynamics model in 3­D underwater environments. IEEE
                   Trans Neural Netw Learn Syst 2016;27:2364–74.
               84.  Cao X, Zhu D. Multi­AUV underwater cooperative search algorithm based on biological inspired neurodynamics model and velocity
                   synthesis. J Navig 2015;68:1075–87.
               85.  Huang Z, Zhu D. A cooperative hunting algorithm of multi­AUV in 3­D dynamic environment. In: The 27th Chinese Control and
                   Decision Conference (2015 CCDC); 2015 May 23­25; Qingdao, China. IEEE; 2015. pp. 2571–75.
               86.  Zhu D, Lv R, Cao X, Yang SX. Multi­AUV hunting algorithm based on bio­inspired neural network in unknown environments. Int J
                   Adv Robot Syst 2015;12:166.
               87.  Cao X, Huang Z, Zhu D. AUV cooperative hunting algorithm based on bio­inspired neural network for path conflict state. In:
                   2015 IEEE International Conference on Information and Automation; 2015 Aug 8­10; Lijang, China. IEEE; 2015. pp. 1821–26.
               88.  Yang SX, Yuan G, Meng M, Mittal GS. Real­time collision­free path planning and tracking control of a nonholonomic mobile robot
                   using a biologically inspired approach. In: Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation; 2001
                   May 21­26 ; Seoul, Korea (South). vol. 4. IEEE; 2001. pp. 3402–7.
               89.  Yuan G, Yang SX, Mittal GS. Tracking control of a mobile robot using a neural dynamics based approach. In: Proceedings 2001 ICRA.
                   IEEE International Conference on Robotics and Automation; 2001 May 21­26 ; Seoul, Korea (South). IEEE; 2001. pp. 163–68.
               90.  Zheng W, Wang H, Zhang Z, Wang H. Adaptive robust finite­time control of mobile robot systems with unmeasurable angular velocity
                   via bioinspired neurodynamics approach. Eng Appl Artif Intell 2019;82:330–44.
               91.  Hu Y, Yang SX. A fuzzy neural dynamics based tracking controller for a nonholonomic mobile robot. In: Proceedings 2003 IEEE/ASME
                   International Conference on Advanced Intelligent Mechatronics (AIM 2003); 2003 Jul 20­24 Kobe, Japan. IEEE; 2003. pp. 205–10.
               92.  Zhang HD, Liu SR, Yang SX. A neurodynamics based neuron­PID controller and its application to inverted pendulum. In: Proceedings
                   of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826); 2004 Aug 26­29; Shanghai, China.
                   IEEE; 2004. pp. 527–32.
   82   83   84   85   86   87   88   89   90   91   92