Abstract
In this paper, a hybrid method based on rough sets and genetic algorithms, is proposed to improve the speed of robot path planning. Decision rules are obtained using rough set theory. A series of available paths are produced by training obtained minimal decision rules. Path populations are optimised by using genetic algorithms until the best path is obtained. Experiment results show that this hybrid method is capable of improving robot path planning speed.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
K. Sugibara, J. Smith. Genetic algorithms for adaptive motion planning of an autonomous mobile robot. In Proceedings of 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation, Hawaii, HI, USA, IEEE Press, pp. 138–143, 1997.
H. Surmann, J. Huser, J. Wehking. Path planning for a fuzzy controlled autonomous mobile robot. In Proceedings of the 5th IEEE International Conference on Fuzzy Systems, St.Augustin, Germany, IEEE Press, vol. 8–11, pp. 1660–1665, 1996.
J. Yu, K. Valeri, Z. Sun, N. Hiroyuki. Fast algorithm for path planning based on neural networks. Robots, vol. 23, no. 3, pp. 201–205, 2001.
Z. Pawlak. Rough Sets: Theoretical Aspects of Reasoning about Data. Nowowiejska, Warsaw, Poland, pp. 15–19, 1990.
H. D. Zhang, R. Zheng, Y.W. Chen. Present situation and future development of mobile robot path planning technology. Journal of System Simulation, vol. 17, no. 2, pp. 439–443, 2005.
T. Shibata, T. Fukuda. Intelligent motion planning by genetic algorithm with fuzzy critic. In Proceedings of the 8th IEEE International Symposium on Intelligent Control, Tsukuba, Japan, IEEE Press, vol. 25–27, pp. 565–570, 1993.
T. Ozyer, R. Alhajj, K. Barker. Utilizing rough sets and multi-objective genetic algorithms for automated clustering. In Proceedings of 4th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2004, Uppsala, Sweden, vol. 1–5, pp. 567–572, 2004.
M. Inuiguchi, T. Tanino. Generalized rough sets and rule extraction. In Proceedings of the 3rd International Conference on Rough Sets and Current Trends in Computing, RSCTC 2002, Malvern, PA, USA, vol. 14–16, pp. 105–112, 2002.
Z. Cai, Z. Peng. Cooperative coevolutionary adaptive genetic algorithm in path planning of cooperative multi-mobile robot systems. Journal of Intelligent and Robotic Systems, vol. 33, no. 1, pp. 61–71, 2002.
D. Goldberg. Genetic Algorithms in Search, Optimisation and Machine Learning. Addison-Wesley, New York, USA, pp. 1–25, 1989.
D. Beasley, R. D. Bull, R. Ralph. An overview of genetic algorithms. University Computing, vol. 15, no. 2, pp. 58–69 and vol. 15, no. 4, pp. 170–181, 1993.
X. Xu, Y. Chen. A method for trajectory planning of robot manipulators in Cartesian space. In Proceedings of the 3rd World Congress on Intelligent Control and Automation, Indianapolis, USA, IEEE Press, vol. 28, pp. 1220–1225, 2000.
Author information
Authors and Affiliations
Corresponding author
Additional information
This project is partially supported by Science Research Funding from the Education Department of Liaoning Province, China (No.J9906065).
Cheng-Dong WU Ph.D., Professor, received B.Sc. in Electrical Automation from Shenyang Jianzhu University of. in 1983, M.Sc. in Control Theory and its Applications from Tsinghua University in 1988, and Ph.D. in Industrial Automation from Northeastern University in 1994, China, respectively. He is now Director, Institute of Artificial Intelligence and Robotics, Northeatern University, Shenyang, China. He was the principal investigator of over 20 projects in robot control and path planning, image processing, intelligent traffic systems, smart home systems and wireless network communication technologies. He has published 9 books and over 150 journal and conference papers.
Yong Yue is a Principal Lecturer at the University of Luton, UK. He holds a B.Sc. in mechanical engineering from the Northeastern University, China, and a Ph.D. from Heriot-Watt University, UK. He is a Chartered Engineer and a Member of the Institution of Mechanical Engineers, UK. Dr. Yue has been working in academia for 15 years following his 8 years of industrial experience. His main research interests include CAD/CAM, geometric modelling, virtual reality and pattern recognition.
Ying Zhang graduated form Shenyang Jianzhu University, China, in 2003. She received an M.Sc. in Control Theory and Control Engineering from Shenyang Jianzhu University. She is currently pursuing a Ph.D. degree in Shenyang Institute of Automation, China. Her research interests include intelligent robot control, pattern recognition and image processing.
Meng-Xin Li is Associated Professor in the School of Information and Control Engineering, Shenyang Jianzhu University, China. She received her B.Sc. and M.Sc. in Control Theory and Control Engineering from Shenyang Jianzhu University, and her Ph.D. degree in Pattern Recognition from the University of Luton, UK. Her research interests include neural networks, rough set theory and image recognition.
Rights and permissions
About this article
Cite this article
Wu, CD., Zhang, Y., Li, MX. et al. A rough set GA-based hybrid method for robot path planning. Int J Automat Comput 3, 29–34 (2006). https://doi.org/10.1007/s11633-006-0029-5
Received:
Revised:
Issue Date:
DOI: https://doi.org/10.1007/s11633-006-0029-5