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.
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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.
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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
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DOI: https://doi.org/10.1007/s11633-006-0029-5