DLP - Evolutionary Mobile Robots using Computational Intelligence Techniques

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Evolutionary robots, like autonomous artificial organisms, automatically develop their own skills by interaction with the environment. This talk will focus on evolutionary locomotion control of mobile robots using computational intelligence techniques, including fuzzy systems and evolutionary computation. First, the basic concept of evolutionary fuzzy systems (EFSs) will be introduced. Next, for wheeled robots, an obstacle boundary following behavior learned through EFSs will be introduced. Evolutionary fuzzy control of a single wheeled robot and multiple wheeled robots cooperatively carrying an object through multi-objective evolutionary computation algorithms for obstacle boundary following will be introduced. Then, to boost the learning efficiency of multi-objective EFSs in this application, the technique of reinforcement neural fuzzy surrogate-assisted learning will be given. Finally, navigation of a single and multiple cooperative wheeled robots in unknown environments will be presented. 


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  • Date: 14 Sep 2023
  • Time: 10:00 AM to 11:30 AM
  • All times are (UTC+08:00) Kuala Lumpur
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  • Starts 01 September 2023 06:17 PM
  • Ends 13 September 2023 06:17 PM
  • All times are (UTC+08:00) Kuala Lumpur
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  Speakers

Prof Chia-Feng Juang

Topic:

Evolutionary Mobile Robots using Computational Intelligence Techniques

Evolutionary robots, like autonomous artificial organisms, automatically develop their own skills by interaction with the environment. This talk will focus on evolutionary locomotion control of mobile robots using computational intelligence techniques, including fuzzy systems and evolutionary computation. First, the basic concept of evolutionary fuzzy systems (EFSs) will be introduced. Next, for wheeled robots, an obstacle boundary following behavior learned through EFSs will be introduced. Evolutionary fuzzy control of a single wheeled robot and multiple wheeled robots cooperatively carrying an object through multi-objective evolutionary computation algorithms for obstacle boundary following will be introduced. Then, to boost the learning efficiency of multi-objective EFSs in this application, the technique of reinforcement neural fuzzy surrogate-assisted learning will be given. Finally, navigation of a single and multiple cooperative wheeled robots in unknown environments will be presented. 

Biography:

Prof. Chia-Feng Juang received the B.S. and Ph.D. degrees in Control Engineering from the National Chiao-Tung University, Hsinchu, Taiwan, in 1993 and 1997, respectively. Since 2001, he has been with the Department of Electrical Engineering, National Chung Hsing University, Taichung, Taiwan, where he became a Full Professor in 2007 and has been a Distinguished Professor since 2009. He served as the Chapter Chair of IEEE Computational Intelligence, Taipei Chapter, in 2017-2018, during which the chapter won the Outstanding Chapter Award from IEEE Taipei Session. Prof. Juang has authored or coauthored over 110 journal papers (including over 60 IEEE journal papers), ten book chapters, and over 130 conference papers. His current research interests include computational intelligence, intelligent control, computer vision, and intelligent robots. 
Prof. Juang received the Outstanding Automatic Control Engineering Award from Chinese Automatic Control Society, Taiwan, in 2014; the Outstanding Electrical Engineering Professor Award from Chinese Institute of Electrical Engineering, Taiwan, in 2019; and the Outstanding Research Award from Ministry of Science and Technology, Taiwan, in 2021. He was elevated to IEEE Fellow in 2019 and IFSA Fellow in 2023. He is a Distinguished Lecture of IEEE Computational Intelligence Society. He is an Associate Editor of the IEEE TRANSACTIONS ON FUZZY SYSTEMS, the IEEE TRANSACTIONS ON CYBERNETICS, and the Asian Journal of Control and an Area Editor of the International Journal of Fuzzy Systems.