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A new approach to improve the parameter estimation accuracy in robotic manipulators using a multi-objective output error identification technique

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Published
Publication date14/12/2017
Host publication26th IEEE International Symposium on Robot and Human Interactive Communication, Lisbon
PublisherIEEE
Pages1406-1411
Number of pages6
ISBN (Electronic)9781538635186
ISBN (Print)9781538635193
Original languageEnglish
Event26th IEEE International Symposium on Robot and Human Interactive Communication - Pestana Palace Hotel, Lisbon, Portugal
Duration: 28/08/20171/09/2017
http://www.ro-man2017.org/site/

Conference

Conference26th IEEE International Symposium on Robot and Human Interactive Communication
Abbreviated titleIEEE RO-MAN2017
CountryPortugal
CityLisbon
Period28/08/171/09/17
Internet address

Conference

Conference26th IEEE International Symposium on Robot and Human Interactive Communication
Abbreviated titleIEEE RO-MAN2017
CountryPortugal
CityLisbon
Period28/08/171/09/17
Internet address

Abstract

The research behind this article primarily concerns the development of mobile robots for nuclear decommissioning. The robotic platform under study has dual, seven-function, hydraulically actuated manipulators, for which the authors have developed a vision based, assisted teleoperation interface for common decommissioning tasks such as pipe cutting. However, to improve safety, task execution speed and operator training-time, high performance control of the nonlinear manipulator dynamics is required. Hence, the present article focuses on an associated dynamic model, and addresses the challenging generic task of parameter estimation for a highly convex and nonlinear system. A novel approach for estimation of the fundamental parameters of the manipulator, based on the idea of multi-objectivization, is proposed. Here, a single objective output error identification problem is converted into a multi-objective optimization problem. This is solved using a multi-objective genetic algorithm with non-dominated sorting. Numerical and experimental results using the nuclear decommissioning robot, show that the performance of the proposed approach, in terms of both the output error index and the accuracy of the estimated parameters, is superior to the previously studied single-objective identification problem.