REFERENCES
1. Bekey GA. Autonomous robots: from biological inspiration to implementation and control. Boston: MIT press; 2005.
2. Li J, Yang SX, Xu Z. A survey on robot path planning using bio-inspired algorithms. In: 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO) Dali, China. IEEE; 2019. pp. 2111-16.
3. Pradhan B, Nandi A, Hui NB, Roy DS, Rodrigues JJPC. A novel hybrid neural network-based multirobot path planning with motion coordination. IEEE Trans Veh Technol 2020;69:1319-27.
4. Huang HC. SoPC-based parallel ACO algorithm and its application to optimal motion controller design for intelligent omnidirectional mobile robots. IEEE Trans Industr Inform 2013;9:1828-35.
5. Roberge V, Tarbouchi M, Labonte G. Fast genetic algorithm path planner for fixed-wing military UAV using GPU. IEEE Trans Aerosp Electron Syst 2018;54:2105-17.
6. Hu E, Yang SX, Chiu DKY. A non-time based tracking controller for multiple nonholonomic mobile robots. In: Proceedings 2002 IEEE International Conference on Robotics and Automation Washington, USA. IEEE; 2002. pp. 3954-59.
7. Huan TT, Kien CV, Anh HPH, Nam NT. Adaptive gait generation for humanoid robot using evolutionary neural model optimized with modified differential evolution technique. Neurocomputing 2018;320:112-20.
8. Guo K, Pan Y, Yu H. Composite learning robot control with friction compensation: a neural network-based Approach. IEEE Trans Ind Electron 2019;66:7841-51.
9. Zhang Z, Yan Z. A varying parameter recurrent neural network for solving nonrepetitive motion problems of redundant robot manipulators. IEEE Trans Control Syst Technol 2019;27:2680-87.
10. Hu Y, Yang SX. A knowledge based genetic algorithm for path planning of a mobile robot. In: IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA’04 New Orleans, USA. vol. 5. IEEE; 2004. pp. 4350-55.
11. Zeng Y, Li J, Yang S, Ren E. A bio-inspired control strategy for locomotion of a quadruped robot. Applied Sciences 2018;8:56.
12. Grossberg S. Contour enhancement, short term memory, and constancies in reverberating neural networks. Stud Appl Math 1973;52:213-57.
13. Yang SX, Meng M. Neural network approaches to dynamic collision-free trajectory generation. IEEE Trans Syst Man Cybern B Cybern 2001;31:302-18.
14. Yang SX, Zhu A, Yuan G, Meng MQ. A bioinspired neurodynamics-based approach to tracking control of mobile robots. IEEE Trans Consum Electron 2012;59:3211-20.
15. Yang SX, Meng MQH. Real-time collision-free motion planning of a mobile robot using a neural dynamics-based approach. IEEE Trans Neural Netw 2003;14:1541-52.
16. Zhu A, Yang SX. Path planning of multi-robot systems with cooperation. In: Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium 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;SMC-13:815-26.
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 ON-OFF 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. Real-time path planning and tracking control using a neural dynamics based approach. IFAC Proceedings Volumes 2002. pp. 103-8.
25. Ni J, Wu L, Shi P, Yang SX. A dynamic bioinspired neural network based real-time 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 multi-robot formation control in unknown environments. Int J Rob Autom 2015;30.
27. Oh H, Shirazi AR, Sun C, Jin Y. Bio-inspired self-organising multi-robot 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 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 Yasmine Hammamet, Tunisia. IEEE; 2002. pp. 515-20.
31. Yang SX, Meng M, Li H. A neural computation model for real-time collision-free robot navigation. IFAC Proceedings Volumes 2002. pp. 323-28.
32. Yang X, Meng M. An efficient neural network model for path planning of car-like robots in dynamic environment. In: Engineering Solutions for the Next Millennium. 1999 IEEE Canadian Conference on Electrical and Computer Engineering (Cat. No.99TH8411) Edmonton,Canada. IEEE; 1999. pp. 1374-79.
33. Yang SX, Meng M, Yuan X. A biological inspired neural network approach to real-time collision-free motion planning of a nonholonomic car-like robot. In: Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113) 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 multi-scale 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 network-based Q-learning 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) Waikoloa, USA. IEEE; 2014. pp. 829-33.
38. Chen Y, Xu W, Li Z, et al. Safety-enhanced 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 real-time path planning with safety consideration. In: SMC’98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics San Diego, USA. IEEE; 1998. pp. 3412-17.
40. Yang SX, Meng M. An efficient neural network method for real-time 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.
42. Sun B, Zhu D, Tian C, Luo C. Complete coverage autonomous underwater vehicles path planning based on glasius bio-inspired neural network algorithm for discrete and centralized programming. IEEE Trans Cogn Commun Netw 2019;11:73-84.
43. Chen M, Zhu D. Multi-AUV cooperative hunting control with improved Glasius bio-inspired neural network. J Navig 2018;72:759-76.
44. Chen M, Zhu D. Real-time path planning for a robot to track a fast moving target based on improved Glasius bio-inspired neural networks. Int J Intell Robot Appl 2019;3:186-95.
45. Willms AR, Yang SX. An efficient dynamic system for real-time robot-path planning. IEEE Trans Syst Man Cybern B Cybern 2006;36:755-66.
46. Willms AR, Yang SX. Real-time robot path planning via a distance-propagating dynamic system with obstacle clearance. IEEE Trans Syst Man Cybern B Cybern 2008;38:884-93.
47. Li S, Meng MQH, Chen W, et al. SP-NN: A novel neural network approach for path planning. In: 2007 IEEE Interna- tional Conference on Robotics and Biomimetics (ROBIO) Sanya, China. IEEE; 2007. pp. 1355-60.
48. Qu H, Yang SX, Willms AR, Yi Z. Real-time robot path planning based on a modified pulse-coupled neural network model. IEEE Trans Neural Netw 2009;20:1724-39.
49. Qu H, Yi Z, Yang SX. Efficient shortest-path-tree computation in network routing based on pulse-coupled neural networks. IEEE Trans Cybern 2013;43:995-010.
50. Zhong Y, Shirinzadeh B, Tian Y. A new neural network for robot path planning. In: 2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics Xi’an, China. IEEE; 2008. pp. 1361-66.
51. Chen Y, Liang J, Wang Y, et al. Autonomous mobile robot path planning in unknown dynamic environments using neural dynamics. Soft Comput 2020;24:13979-95.
52. Bueckert J, Yang SX, Yuan X, Meng MQH. Neural dynamics based multiple target path planning for a mobile robot. In: 2007 IEEE International Conference on Robotics and Biomimetics (ROBIO) Sanya, China. IEEE; 2007. pp. 1047-52.
53. Li H, Yang SX, Biletskiy Y. Neural network based path planning for a multi-robot system with moving obstacles. In: 2008 IEEE International Conference on Automation Science and Engineering Arlington, USA. IEEE; 2008. pp. 410-19.
54. Yuan X, Yang SX. Multirobot-based nanoassembly planning with automated path generation. IEEE ASME Trans Mechatron 2007;12:352-56.
55. Zhu A, Cai G, Yang SX. Theoretical analysis of a neural dynamics based model for robot trajectory generation. In: IEEE 2002 International Conference on Communications, Circuits and Systems and West Sino Expositions Chengdu, China. IEEE; 2002. pp. 1184-88.
56. Ni J, Yang SX. Bioinspired neural network for real-time cooperative hunting by multirobots in unknown environments. IEEE Trans Neural Netw 2011;22:2062-77.
57. Yang SX, Luo C. A neural network approach to complete coverage path planning. IEEE Trans Syst Man Cybern B Cybern 2004;34:718-24.
58. Godio S, Primatesta S, Guglieri G, Dovis F. A bioinspired neural network-based approach for cooperative coverage planning of UAVs. Information 2021;12:51.
59. Luo C, Yang SX, Yuan X. Real-time area-covering operations with obstacle avoidance for cleaning robots. In: IEEE/RSJ International Conference on Intelligent Robots and System Lausanne, Switzerland. IEEE; 2002. pp. 2359-64.
60. Yang SX, Luo C, Meng M. A neural computational algorithm for coverage path planning in changing environments. In: IEEE 2002 International Conference on Communications, Circuits and Systems and West Sino Expositions Chengdu, China. IEEE; 2002. pp. 1174-78.
61. Luo C, Yang SX. A real-time cooperative sweeping strategy for multiple cleaning robots. In: Proceedings of the IEEE Internatinal Symposium on Intelligent Control Vancouver, Canada. IEEE; 2002. pp. 660-65.
62. Zhang J, Lv H, He D, et al. Discrete bioinspired neural network for complete coverage path planning. Int J Rob Autom 2017;32.
63. Luo C, Yang SX. A bioinspired neural network for real-time concurrent map building and complete coverage robot navigation in unknown environments. IEEE Trans Neural Netw 2008;19:1279-98.
64. Luo C, Yang SX, Meng MQH. Real-time map building and area coverage in unknown environments. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation Barcelona, Spain. IEEE; 2005. pp. 1736-41.
65. Luo C, Yang S, Meng M. Neurodynamics based complete coverage navigation with real-time map building in unknown environments. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems Beijing, China. IEEE; 2006. pp. 4228-33.
66. Luo C, Yang SX, Li X, Meng MQH. Neural-dynamics-driven complete area coverage navigation through cooperation of multiple mobile robots. IEEE Trans Consum Electron 2017;64:750-60.
67. Yu Z, Tao J, Xiong J, Luo A, Yang SX. Neural-dynamics-based path planning of a bionic robotic Fish. In: 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO) 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 3-D bio-inspired 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. Bio-inspired neural network-based 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 D-S information fusion map building and bio-inspired neural network in unknown dynamic environment. Int J Adv Robot Syst 2014;11:34.
72. Cao X, Peng J. A potential field bio-inspired neural network control algorithm for AUV path planning. In: 2018 IEEE International Conference on Information and Automation (ICIA) 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 bio-Inspired 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 SOM-based approach to dynamic task assignment of multi-robots. In: 2010 8th World Congress on Intelligent Control and Automation Jinan, China. IEEE; 2010. pp. 2168-73.
76. Yi X, Zhu A, Yang SX, Luo C. A bio-inspired approach to task assignment of swarm robots in 3-D dynamic environments. IEEE Trans Cybern 2017;47:974-83.
77. Zhu D, Huang H, Yang SX. Dynamic task assignment and path planning of multi-AUV system based on animproved self-organizing map and velocity synthesis method in three-dimensional underwater workspace. IEEE Trans Cybern 2013;43:504-14.
78. Huang H, Zhu D, Yuan F. Dynamic task assignment and path planning for multi-AUV system in 2D variable ocean current environment. In: 2012 24th Chinese Control and Decision Conference (CCDC) Taiyuan, China. IEEE; 2012. pp. 999-012.
79. Zhu D, Cao X, Sun B, Luo C. Biologically inspired self-organizing 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. Multi-AUV task assignment and path planning with ocean current based on biological inspired self-organizing map and velocity synthesis algorithm. Intelligent Automation & Soft Computing 2015;23:31-39.
81. Zhu D, Zhou B, Yang SX. A novel algorithm of multi-AUVs 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 bio-inspired model. In: The 26th Chinese Control and Decision Conference (2014 CCDC) Changsha, China. IEEE; 2014. pp. 4569-74.
83. Cao X, Zhu D, Yang SX. Multi-AUV target search based on bioinspired neurodynamics model in 3-D underwater environments. IEEE Trans Neural Netw Learn Syst 2016;27:2364-74.
84. Cao X, Zhu D. Multi-AUV 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 multi-AUV in 3-D dynamic environment. In: The 27th Chinese Control and Decision Conference (2015 CCDC) Qingdao, China. IEEE; 2015. pp. 2571-75.
86. Zhu D, Lv R, Cao X, Yang SX. Multi-AUV hunting algorithm based on bio-inspired 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 bio-inspired neural network for path conflict state. In: 2015 IEEE International Conference on Information and Automation Lijang, China. IEEE; 2015. pp. 1821-26.
88. Yang SX, Yuan G, Meng M, Mittal GS. Real-time collision-free 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 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 Seoul, Korea (South). IEEE; 2001. pp. 163-68.
90. Zheng W, Wang H, Zhang Z, Wang H. Adaptive robust finite-time 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) Kobe, Japan. IEEE; 2003. pp. 205-10.
92. Zhang HD, Liu SR, Yang SX. A neurodynamics based neuron-PID controller and its application to inverted pendulum. In: Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826) Shanghai, China. IEEE; 2004. pp. 527-32.
93. Li H, Yang SX, Karray F. Optimization of a neural dynamics based controller for a nonholonomic mobile robot using genetic algorithms. In: The Fourth International Conference on Control and Automation, 2003. ICCA Montreal,Canada. IEEE; 2003. pp. 911-16.
94. Yang SX, Yang H, Meng MQH. Neural dynamics based full-state tracking control of a mobile robot. In: IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA ‘04 New Orleans, USA. IEEE; 2004. pp. 4614-19.
95. Xu Z, Yang SX, Gadsden SA. Enhanced bioinspired backstepping control for a mobile robot with unscented kalman filter. IEEE Magazines and Online Publications 2020;8:125899-908.
96. Pan CZ, Lai XZ, Yang SX, Wu M. A biologically inspired approach to tracking control of underactuated surface vessels subject to unknown dynamics. Expert Syst Appl 2015;42:2153-61.
97. Mohd Shamsuddin BPNF, Bin Mansor MA. Motion cntrol algorithm for path following and trajectory tracking for unmanned surface vehicle: a review paper. In: 2018 3rd International Conference on Control, Robotics and Cybernetics (CRC). Proceedings, Piscataway, NJ, USA 2018. pp. 73-77.
98. Pan CZ, Lai XZ, Yang SX, Wu M. Backstepping neurodynamics based position-tracking control of underactuated autonomous surface vehicles. In: 2013 25th Chinese Control and Decision Conference (CCDC) Guiyang, China. IEEE; 2013. pp. 2845-50.
99. Pan C, Lai X, Yang SX, Wu M. A bioinspired neural dynamics-based approach to tracking control of autonomous surface vehicles subject to unknown ocean currents. Neural Comput Appl 2015;26:1929-38.
100. Li D, Wang P, Du L. Path planning technologies for autonomous underwater vehicles-a review. IEEE Access 2019;7:9745-9768.
101. Burdinsky IN. Guidance algorithm for an autonomous unmanned underwater vehicle to a given target. Optoelectron Instrum Data Process 2012;48:69-74.
102. Karkoub M, Wu HM, Hwang CL. Nonlinear trajectory-tracking control of an autonomous underwater vehicle. Ocean Eng 2017;145:188-98.
103. Zhu D, Hua X, Sun B. A neurodynamics control strategy for real-time tracking control of autonomous underwater vehicle. J Navig 2013;67:113-27.
104. Sun B, Zhu D, Ding F, Yang SX. A novel tracking control approach for unmanned underwater vehicles based on bio-inspired neurodynamics. IJ Mar Sci Tech-japan 2012;18:63-74.
105. Sun B, Zhu D, Yang SX. A bioinspired filtered backstepping tracking control of 7000-m manned submarine vehicle. IEEE Trans Ind Electron 2014;61:3682-93.
106. Jiang Y, Guo C, Yu H. Robust trajectory tracking control for an underactuated autonomous underwater vehicle based on bioinspired neurodynamics. Int J Adv Robot Syst 2018;15:172988141880674.
107. Peng Z, Wen G, Rahmani A, Yu Y. Leader–follower formation control of nonholonomic mobile robots based on a bioinspired neurodynamic based approach. Robot Auton Syst 2013;61:988-96.
108. Yi G, Mao J, Wang Y, Zhang H, Miao Z. Neurodynamics-based leader-follower formation tracking of multiple nonholonomic vehicles. Assembly Autom 2018;38:548-57.
109. He Y, Mou J, Chen L, et al. Survey on hydrodynamic effects on cooperative control of Maritime Autonomous Surface Ships. Ocean Eng 2021;235.
110. Peng Z, Wang J, Wang D, Han QL. An overview of recent advances in coordinated control of multiple autonomous surface vehicles. IEEE Trans Industr Inform 2021;17:732-45.
111. Wang D, Fu M. Adaptive formation control for waterjet USV with input and output constraints based on bioinspired neurodynamics. IEEE Access 2019;7:165852-61.
112. Wang D, Ge SS, Fu M, Li D. Bioinspired neurodynamics based formation control for unmanned surface vehicles with line-of-sight range and angle constraints. Neurocomputing 2021;425:127-34.
113. Yang Y, Xiao Y, Li T. A survey of autonomous underwater vehicle formation: performance, formation control, and communication capability. IEEE Commun Surv Tutor 2021;23:815-41.
114. Hadi B, Khosravi A, Sarhadi P. A review of the path planning and formation control for multiple autonomous underwater vehicles. J Intel Robot Syst 2021;101.