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Authors: Enrico Villagrossi 1 ; Giovanni Legnani 2 ; Nicola Pedrocchi 3 ; Federico Vicentini 3 ; Lorenzo Molinari Tosatti 3 ; Fabio Abbà 4 and Aldo Bottero 4

Affiliations: 1 National Research Council and University of Brescia, Italy ; 2 University of Brescia, Italy ; 3 National Research Council, Italy ; 4 COMAU Robotics, Italy

Keyword(s): Industrial Robot Dynamics Identification, Optimal Excitation Trajectories, Dynamics Decoupling.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Genetic Algorithms ; Industrial Automation and Robotics ; Industrial Engineering ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Modeling, Simulation and Architectures ; Robotics and Automation ; Signal Processing, Sensors, Systems Modeling and Control ; Soft Computing ; System Identification

Abstract: Robot dynamics is commonly modeled as a linear function of the robot kinematic state from a set of dynamic parameters into motor torques. Base parameters (i.e. the set of theoretically demonstrated linearly-independent parameters) can be reduced to a subset of "essential" parameters by eliminating those that are negligible with respect to their contribution in motor torques. However, generic trajectories, if not properly defined, couple the contribution of such essential parameters into the motor torques, actually reducing the estimation accuracy of the dynamics parameters. The work presented here introduces an index for evaluating correlation influence among essential parameters along an executed trajectory. Such index is then exploited for an optimal search of excitatory patterns consistent with the kinematical coupling constraints. The method is experimentally compared with the results achievable by one of the most popular IRs dynamic calibration method.

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Paper citation in several formats:
Villagrossi, E.; Legnani, G.; Pedrocchi, N.; Vicentini, F.; Molinari Tosatti, L.; Abbà, F. and Bottero, A. (2014). Robot Dynamic Model Identification Through Excitation Trajectories Minimizing the Correlation Influence among Essential Parameters. In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-040-6; ISSN 2184-2809, SciTePress, pages 475-482. DOI: 10.5220/0005060704750482

@conference{icinco14,
author={Enrico Villagrossi. and Giovanni Legnani. and Nicola Pedrocchi. and Federico Vicentini. and Lorenzo {Molinari Tosatti}. and Fabio Abbà. and Aldo Bottero.},
title={Robot Dynamic Model Identification Through Excitation Trajectories Minimizing the Correlation Influence among Essential Parameters},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2014},
pages={475-482},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005060704750482},
isbn={978-989-758-040-6},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Robot Dynamic Model Identification Through Excitation Trajectories Minimizing the Correlation Influence among Essential Parameters
SN - 978-989-758-040-6
IS - 2184-2809
AU - Villagrossi, E.
AU - Legnani, G.
AU - Pedrocchi, N.
AU - Vicentini, F.
AU - Molinari Tosatti, L.
AU - Abbà, F.
AU - Bottero, A.
PY - 2014
SP - 475
EP - 482
DO - 10.5220/0005060704750482
PB - SciTePress