Please use this identifier to cite or link to this item: https://doi.org/10.1162/0899766053630378
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dc.titleAn ensemble of cooperative extended kohonen maps for complex robot motion tasks
dc.contributor.authorLow, K.H.
dc.contributor.authorLeow, W.K.
dc.contributor.authorAng Jr., M.H.
dc.date.accessioned2013-07-23T09:25:38Z
dc.date.available2013-07-23T09:25:38Z
dc.date.issued2005
dc.identifier.citationLow, K.H., Leow, W.K., Ang Jr., M.H. (2005). An ensemble of cooperative extended kohonen maps for complex robot motion tasks. Neural Computation 17 (6) : 1411-1445. ScholarBank@NUS Repository. https://doi.org/10.1162/0899766053630378
dc.identifier.issn08997667
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/43124
dc.description.abstractSelf-organizing feature maps such as extended Kohonen maps (EKMs) have been very successful at learning sensorimotor control for mobile robot tasks. This letter presents a new ensemble approach, cooperative EKMs with indirect mapping, to achieve complex robot motion. An indirect-mapping EKM self-organizes to map from the sensory input space to the motor control space indirectly via a control parameter space. Quantitative evaluation reveals that indirect mapping can provide finer, smoother, and more efficient motion control than does direct mapping by operating in a continuous, rather than discrete, motor control space. It is also shown to outperform basis function neural networks. Furthermore, training its control parameters with recursive least squares enables faster convergence and better performance compared to gradient descent. The cooperation and competition of multiple self-organized EKMs allow a nonholonomic mobile robot to negotiate unforeseen, concave, closely spaced, and dynamic obstacles. Qualitative and quantitative comparisons with neural network ensembles employing weighted sum reveal that our method can achieve more sophisticated motion tasks even though the weighted-sum ensemble approach also operates in continuous motor control space. © 2005 Massachusetts Institute of Technology.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1162/0899766053630378
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1162/0899766053630378
dc.description.sourcetitleNeural Computation
dc.description.volume17
dc.description.issue6
dc.description.page1411-1445
dc.identifier.isiut000228694000007
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