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dc.contributor.authorZalana, Eduardoen_US
dc.contributor.authorGaudiano, Paoloen_US
dc.contributor.authorCoronado, Juan Lopezen_US
dc.date.accessioned2011-11-14T18:45:28Z
dc.date.available2011-11-14T18:45:28Z
dc.date.issued1994-01en_US
dc.identifier.urihttp://hdl.handle.net/2144/2140
dc.description.abstractThis article introduces a real-time, unsupervised neural network that learns to control a two-degree-of-freedom mobile robot in a nonstationary environment. The neural controller, which is termed neural NETwork MObile Robot Controller (NETMORC), combines associative learning and Vector Associative Map (YAM) learning to generate transformations between spatial and velocity coordinates. As a result, the controller learns the wheel velocities required to reach a target at an arbitrary distance and angle. The transformations are learned during an unsupervised training phase, during which the robot moves as a result of randomly selected wheel velocities. The robot learns the relationship between these velocities and the resulting incremental movements. Aside form being able to reach stationary or moving targets, the NETMORC structure also enables the robot to perform successfully in spite of disturbances in the enviroment, such as wheel slippage, or changes in the robot's plant, including changes in wheel radius, changes in inter-wheel distance, or changes in the internal time step of the system. Finally, the controller is extended to include a module that learns an internal odometric transformation, allowing the robot to reach targets when visual input is sporadic or unreliable.en_US
dc.description.sponsorshipSloan Fellowship (BR-3122), Air Force Office of Scientific Research (F49620-92-J-0499)en_US
dc.language.isoen_USen_US
dc.publisherBoston University Center for Adaptive Systems and Department of Cognitive and Neural Systemsen_US
dc.relation.ispartofseriesBU CAS/CNS Technical Reports;CAS/CNS-TR-1994-002en_US
dc.rightsCopyright 1994 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission.en_US
dc.subjectAssociative learningen_US
dc.subjectDIRECT modelen_US
dc.subjectInverse kinematicsen_US
dc.subjectOdometric mappingen_US
dc.subjectVAM learningen_US
dc.subjectVITE modelen_US
dc.titleA Real-Time Unsupervised Neural Network for the Low-Level Control of a Mobile Robot in a Nonstationary Environmenten_US
dc.typeTechnical Reporten_US
dc.rights.holderBoston University Trusteesen_US


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