Authors:
Marija Turković
1
;
2
;
Marko Švaco
2
and
Bojan Jerbić
2
Affiliations:
1
Department of Physics, Faculty of Science, University of Zagreb, Bijenička cesta 32, Zagreb, Croatia
;
2
Department of Robotics and Production System Automation, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lučića 5, Zagreb, Croatia
Keyword(s):
Non-parametric Robot Calibration, Neural Networks, Genetic Algorithms, Robot Precision.
Abstract:
In this paper, a novel method for non-parametric robot calibration which uses intelligent algorithms is proposed. The non-parametric calibration should prove very useful, because it does not need to identify the geometric parameters of the robot as is the case in parametric calibration. Instead, only the position measurements need to be provided. This could potentially lead to a cheaper and faster calibration process which could simplify its application on different and unique robot geometries. The biggest issue of using neural networks is that they require a lot of data, while for the process of robot calibration a very limited number of measurements is usually collected. In this experiment, the improvement of the hyperparameters of the neural network was attempted by using the genetic algorithms. Simulations also showed that the parametric optimization converges faster and that feed-forward back-propagating neural networks could not correctly simulate the behaviour of complex robot
s, or problems which used small datasets. However, for simple robot geometries and massive datasets, the neural network successfully simulated the behaviour of the robot. Although the number of measurements needed was well beyond the scope for real world applications, a few possible improvements were suggested for future research.
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