Research Initiation:  A Walsh Associative Memory Based      Adaptive Controller for Robot Manipulators
{'Code': '07030000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'CMMI', 'LongName': 'Div Of Civil, Mechanical, & Manufact Inn'}}
{'SignBlockName': 'K. (Cheena) Srinivasan', 'PO_EMAI': None, 'PO_PHON': None}
The main objective of this research is to explore the development of an         intelligent memory that can be trained to remember the previous control         commands for various trajectories of a robot manipulator.  In contrast          to the conventional analytical approach, this technique will provide a          real-time, learning, and adaptive control which does not require any            accurate models of the system.  In initial work, a simple                       three-degree-of-freedom manipulator resembling the Stanford arm was             used as a model to generate a thousand quantified trajectory points for         a training set, and a simple averaging interpolation formula was used           to estimate joint commands for other trajectories.  A hierarchical              (tree) structure was explored for storing the configuration space joint         data for the training set.
{'FirstName': 'Se-Young', 'LastName': 'Oh', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Se-Young Oh', 'EmailAddress': None, 'NSF_ID': '000279031', 'StartDate': '09/01/1985', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Florida', 'CityName': 'GAINESVILLE', 'ZipCode': '326111941', 'PhoneNumber': '3523923516', 'StreetAddress': '1523 UNION RD RM 207', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Florida', 'StateCode': 'FL', 'CONGRESSDISTRICT': '03', 'CONGRESS_DISTRICT_ORG': 'FL03', 'ORG_UEI_NUM': 'NNFQH1JAPEP3', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF FLORIDA', 'ORG_PRNT_UEI_NUM': None}
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{'Code': '145100', 'Text': 'AUTO, INSTRUMENT & SENSING SYS'}
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