Page 84 - Read Online
P. 84
Page 79 Li et al. Intell Robot 2021;1(1):58-83 I http://dx.doi.org/10.20517/ir.2021.08
DECLARATIONS
Authors’ contributions
Made substantial contributions to the research and investigation process, reviewed and summarized the lit-
erature, wrote and edited the original draft: Li J, Xu Z, Zhu D
Made substantial contributions to review and summarize the literature: Dong K, Yan T, Zeng Z
Performed oversight and leadership responsibility for the research activity planning and execution as well as
developed ideas and provided critical review, commentary and revision: Yang SX
Availability of data and materials
Not applicable.
Financial support and sponsorship
This work was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada.
Conflicts of interest
All authors declared that there are no conflicts of interest.
Ethical approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Copyright
© The Author(s) 2021.
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 bioinspired algorithms. In: 2019 IEEE International Conference on Robotics
and Biomimetics (ROBIO); 2019 Dec 68; Dali, China. IEEE; 2019. pp. 2111–16.
3. Pradhan B, Nandi A, Hui NB, Roy DS, Rodrigues JJPC. A novel hybrid neural networkbased multirobot path planning with motion
coordination. IEEE Trans Veh Technol 2020;69:1319–27.
4. Huang HC. SoPCbased 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 fixedwing military UAV using GPU. IEEE Trans Aerosp
Electron Syst 2018;54:2105–17.
6. Hu E, Yang SX, Chiu DKY. A nontime based tracking controller for multiple nonholonomic mobile robots. In: Proceedings 2002
IEEE International Conference on Robotics and Automation; 2002 May 1115 ; 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 networkbased 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 manipula
tors. 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; 2004 Apr 26 May 1; New Orleans, USA. vol. 5. IEEE; 2004. pp. 4350–55.
11. Zeng Y, Li J, Yang S, Ren E. A bioinspired 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 collisionfree trajectory generation. IEEE Trans Syst Man Cybern B Cybern
2001;31:302–18.
14. Yang SX, Zhu A, Yuan G, Meng MQ. A bioinspired neurodynamicsbased approach to tracking control of mobile robots. IEEE Trans