Mobile robot automatic navigation control algorithm based on fuzzy neural network in industrial Internet of things environment
https://doi.org/10.32517/0234-0453-2020-35-1-59-67
Abstract
Mobile robot is an important developing direction in the field of robotics, it is widely used in Industrial Internet of Things (IIoT) environment, agriculture, military, transportation, services with the coming of 5G wireless communication technology. Automatic navigation control technology is the core in these research areas, which is also the key technology for mobile robot to achieve intelligentization and autonomation.
The article discusses and researches the neural network technology and its application in mobile robot navigation control. For the characteristics and research of mobile robot navigation problem, it finds the way to improve the mobile robot intelligentization, level of the self-organization, self-learning and adaptive capability. The combination of neural network with other intelligent algorithms solves autonomous navigation problem of the mobile robot in the complex uncertain environments and unknown variable environments. The mobile robot navigation control problem using fuzzy neural network can achieve a more effective real-time navigation control performance through amending the network weights by self-study according to the navigation priori knowledge of human experts.
About the Authors
P. PeiBelarus
Pei Ping, postgraduate student at the Department “Information Systems and Technologies”, International Institute of Distant Education
Yu. N. Petrenko
Belarus
Yury N. Petrenko, IEEE Life Member, Candidate of Sciences (Engineering), Docent, Associate Professor at the Department “Information Systems and Technologies”, International Institute of Distant Education
References
1. Ping P., Petrenko Y. N. Аlgoritm gibkogo upravleniya mnozhestvennym dostupom v mobil’noj besprovodnoj sisteme 5G [The flexible multiple access control algorithm in 5th generation mobile wireless system]. Informatika — Informatics, 2018, vol. 15, no. 3, p. 93–101. (In Russian.) Available at: https://inf.grid.by/jour/article/view/419
2. KhatibO. Real-time obstacle avoidance for manipulators and mobile robots. Proc. 1985 IEEE Int. Conf. on Robotics and Automation. IEEE, 1985, vol. 5, no. 1, p. 500–505. DOI: 10.1109/ROBOT.1985.1087247
3. Khosla P., Volpe R. Superquadric artificial potentials for obstacle avoidance and approach. Proc. 1988 IEEE Int. Conf. on Robotics and Automation. IEEE, 1988, vol. 10, no. 2, p. 1778–1784. DOI: 10.1109/ROBOT.1988.12323
4. Glasius R., Komoda A., Gielen S. C. A. M. A biologically inspired neural net for trajectory formation and obstacle avoidance. Biological Cybernetics, 1996, vol. 74, p. 511–520. DOI: 10.1007/BF00209422
5. Leon J. A. F., Tosini M., Acosta G. G. Evolutionary reactive behavior for mobile robots navigation. Proc. IEEE Conf. on Cybernetics and Intelligent Systems. IEEE, 2004, p. 532–537. DOI: 10.1109/ICCIS.2004.1460471
6. Tuci E., Harvey I., Quinn M. Evolving integrated controllers for autonomous learning robots using dynamic neural networks. ICSAB: Proc. Seventh Int. Conf. on simulation of adaptive behavior on From animals to animats. 2002, p. 282–291.
7. Holland J. H. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, 1992. 228 p.
8. Sutton R. S., Barto A. G. Reinforcement learning: an introduction. MIT Press, 1998. 344 p.
9. Tan K. C., Tan K. K., Lee T. H., Zhao S., Chen Y. J. Autonomous robot navigation based on fuzzy sensor fusion and reinforcement learning. Proc. IEEE Int. Symposium on Intelligent Control. IEEE, 2002, vol. 28, no. 6, p. 182–187. DOI: 10.1109/ISIC.2002.1157759
10. CarpenterG.A., GrossbergS. ART 2: Self-Organization of stable category recognition codes for analog input patterns. Applied Optics, 1987, vol. 26, is. 23, p. 4919–4930. DOI: 10.1364/AO.26.004919
11. Jianhong L., Dezhao C. An enhanced ART2 neural network for clustering analysis. Proc. First Int. Workshop on Knowledge Discovery and Data Mining. IEEE, 2008, p. 81–85. DOI: 10.1109/WKDD.2008.117
12. Qian X.-D., Wang Z.-O., Wang Y. A method of data clustering based on improved algorithm of ART2. Proc. 2005 Int. Conf. on Machine Learning and Cybernetics. IEEE, 2005, p. 2021–2026. DOI: 10.1109/ICMLC.2005.1527277
13. Leon J. A. F., Tosini M., Acosta G. G. Evolutionary reactive behavior for mobile robots navigation. IEEE Conference on Cybernetics and Intelligent System. IEEE, 2004, p. 532–537. DOI: 10.1109/ICCIS.2004.1460471
14. Nelson A. L., Grant E., Galeotti J. M., Rhody S. Maze exploration behaviors using an integrated evolutionary robotic environment. Robotics and Autonomous Systems, 2004, vol. 46, is. 3, p. 159–173. DOI: 10.1016/j.robot.2003.11.002
15. Asada M., Noda S., Tawaratsumida S., Hosoda K. Purposive behavior acquisition for a real robot by vision-based reinforcement learning. Machine Learning, 1996, vol. 23, p. 279–303. DOI: 10.1023/A:1018237008823
16. Cliff D., Husbands P., Harvey I. Explorations in evolutionary robotics. Adaptive Behavior, 1993, vol. 2, is. 1, p. 73–110. DOI: 10.1177/105971239300200104
17. Del R., Millan J. Rapid, safe and incremental learning of navigation strategies. Proc. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). IEEE, 1996, vol. 26, is. 3, p. 408–420. DOI: 10.1109/3477.499792
18. Mitchell T.M. Machine learning. New York, McGrawHill, 1997. 432 p.
Review
For citations:
Pei P., Petrenko Yu.N. Mobile robot automatic navigation control algorithm based on fuzzy neural network in industrial Internet of things environment. Informatics and education. 2020;1(1):59-67. https://doi.org/10.32517/0234-0453-2020-35-1-59-67