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TZID:Asia/Kuala_Lumpur
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DTSTART:20380119T111407
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DTSTAMP:20230921T140839Z
UID:8A6523CD-EC21-468B-A919-84DE9DD84520
DTSTART;TZID=Asia/Kuala_Lumpur:20230914T100000
DTEND;TZID=Asia/Kuala_Lumpur:20230914T113000
DESCRIPTION:Evolutionary robots\, like autonomous artificial organisms\, au
 tomatically develop their own skills by interaction with the environment. 
 This talk will focus on evolutionary locomotion control of mobile robots u
 sing computational intelligence techniques\, including fuzzy systems and e
 volutionary computation. First\, the basic concept of evolutionary fuzzy s
 ystems (EFSs) will be introduced. Next\, for wheeled robots\, an obstacle 
 boundary following behavior learned through EFSs will be introduced. Evolu
 tionary fuzzy control of a single wheeled robot and multiple wheeled robot
 s cooperatively carrying an object through multi-objective evolutionary co
 mputation algorithms for obstacle boundary following will be introduced. T
 hen\, to boost the learning efficiency of multi-objective EFSs in this app
 lication\, the technique of reinforcement neural fuzzy surrogate-assisted 
 learning will be given. Finally\, navigation of a single and multiple coop
 erative wheeled robots in unknown environments will be presented.\n\nSpeak
 er(s): \, Prof Chia-Feng Juang\n\nVirtual: https://events.vtools.ieee.org/
 m/372336
LOCATION:Virtual: https://events.vtools.ieee.org/m/372336
ORGANIZER:wlhoo@um.edu.my 
SEQUENCE:10
SUMMARY:DLP - Evolutionary Mobile Robots using Computational Intelligence T
 echniques
URL;VALUE=URI:https://events.vtools.ieee.org/m/372336
X-ALT-DESC:Description: <br /><p>Evolutionary robots\, like autonomous arti
 ficial organisms\, automatically develop their own skills by interaction w
 ith the environment. This talk will focus on evolutionary locomotion contr
 ol of mobile robots using computational intelligence techniques\, includin
 g 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 m
 ultiple wheeled robots cooperatively carrying an object through multi-obje
 ctive evolutionary computation algorithms for obstacle boundary following 
 will be introduced. Then\, to boost the learning efficiency of multi-objec
 tive EFSs in this application\, the technique of reinforcement neural fuzz
 y surrogate-assisted learning will be given. Finally\, navigation of a sin
 gle and multiple cooperative wheeled robots in unknown environments will b
 e presented.&nbsp\;</p>
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