Neural networks in robotics: state of the art

Tzafestas Spyros, G

dc.contributor.author	Tzafestas Spyros, G	en
dc.date.accessioned	2014-03-01T02:48:20Z
dc.date.available	2014-03-01T02:48:20Z
dc.date.issued	1995	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/33742
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-0029522056&partnerID=40&md5=f546fe46c04b3b316be10b6bedf39b34	en
dc.subject.other	Adaptive control systems	en
dc.subject.other	Dynamics	en
dc.subject.other	Fuzzy control	en
dc.subject.other	Intelligent control	en
dc.subject.other	Kinematics	en
dc.subject.other	Learning systems	en
dc.subject.other	Motion control	en
dc.subject.other	Motion planning	en
dc.subject.other	Robotics	en
dc.subject.other	Neurofuzzy control	en
dc.subject.other	Robot neurocontrol	en
dc.subject.other	Neural networks	en
dc.title	Neural networks in robotics: state of the art	en
heal.type	conferenceItem	en
heal.publicationDate	1995	en
heal.abstract	This paper provides a short review of the neural network approach to system control with reference to robotic systems. Starting with an exposition of the main neurocontrol architectures, the paper overviews the literature on the application of neural networks to robot kinematics, dynamics, path planning and motion control, including some work on neurofuzzy control. To appreciate better robot neurocontrol, an unsupervised robot neurocontroller is presented in some detail. The paper includes a discussion of the criticism made to the neural control paradigms, and an outline of some interesting areas for further research.	en
heal.publisher	IEEE, Piscataway, NJ, United States	en
heal.journalName	IEEE International Symposium on Industrial Electronics	en
dc.identifier.volume	1	en
dc.identifier.spage	12	en
dc.identifier.epage	19	en