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.