2007
DOI: 10.1016/j.asoc.2005.05.007
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Design of a fuzzy controller in mobile robotics using genetic algorithms

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Cited by 66 publications
(33 citation statements)
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“…In order to have an estimation we have considered the two journals with the highest impact factor in the 2012 ThomsonReuters Web of Knowledge (the International Journal of Robotics Research IJRR and IEEE Transactions on Robotics, IEEE-TR) and looked for papers that included the term fuzzy in the Abstract in the period considered (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013). We found that only one paper fulfilled such conditions in IJRR and only seven papers in IEEE-TR.…”
Section: Comments and Conclusionmentioning
confidence: 99%
“…In order to have an estimation we have considered the two journals with the highest impact factor in the 2012 ThomsonReuters Web of Knowledge (the International Journal of Robotics Research IJRR and IEEE Transactions on Robotics, IEEE-TR) and looked for papers that included the term fuzzy in the Abstract in the period considered (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013). We found that only one paper fulfilled such conditions in IJRR and only seven papers in IEEE-TR.…”
Section: Comments and Conclusionmentioning
confidence: 99%
“…There were also some earlier attempts of using the EA-fuzzy approaches in the area of robotics. Mucientes et al [41] realized the automated design of a fuzzy controller using genetic algorithms for the implementation of the wall-following behavior in a mobile robot; Jha et al [42] used a fuzzy logic controller to model the gait generation problem of a two-legged robot and the rule base of the fuzzy controller was optimized offline by a genetic algorithm. The simplified control system block diagram for the driver of a robot arm used here is shown in Fig.…”
Section: Description Of the Optimization Problemmentioning
confidence: 99%
“…Researchers, during the last two decades, have continuously proved that the conventional PID type controllers are not suitable for these types of applications [1,[5][6][7] and several research works have corroborated the fact that fuzzy logic controllers (FLCs) possess tremendous potential to find their applications in these fields [8,10,12]. Several researchers are also motivated by the hybrid design methodologies combining fuzzy control theory with genetic algorithm to accommodate the complexities of the system to be controlled [9,13,18,19]. In this work, a small-scale, real version of a typical industrial thermal process is developed in our laboratory to emulate an air heater thermal process and it presents an excellent opportunity to test our designed stable adaptive fuzzy control methodologies in these situations.…”
Section: Introductionmentioning
confidence: 99%