Vibration Suppression for Improving the Estimation of Kinematic Parameters on Industrial Robots

Vibration is a phenomenon that is present on every industrial system such as CNC machines and industrial robots. Moreover, sensors used to estimate angular position of a joint in an industrial robot are severely affected by vibrations and lead to wrong estimations. This paper proposes a methodology...

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Bibliographic Details
Published in:Shock and vibration 2016-01, Vol.2016 (2016), p.1-15
Main Authors: Morales-Velazquez, Luis, Jaen-Cuellar, Arturo Yosimar, Romero-Troncoso, R. D. J., Elvira-Ortiz, David Alejandro, Osornio-Rios, Roque A.
Format: Article
Language:English
Subjects:
Accuracy
Automation
Filtering
Industrial robots
Kinematics
Manufacturing engineering
Measurement
Mechanical properties
Methodology
Methods
Noise
Robotics
Robots
Robots, Industrial
Sensors
Signal processing
Vibration
Vibration control
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Summary:Vibration is a phenomenon that is present on every industrial system such as CNC machines and industrial robots. Moreover, sensors used to estimate angular position of a joint in an industrial robot are severely affected by vibrations and lead to wrong estimations. This paper proposes a methodology for improving the estimation of kinematic parameters on industrial robots through a proper suppression of the vibration components present on signals acquired from two primary sensors: accelerometer and gyroscope. A Kalman filter is responsible for the filtering of spurious vibration. Additionally, a sensor fusion technique is used to merge information from both sensors and improve the results obtained using each sensor separately. The methodology is implemented in a proprietary hardware signal processor and tested in an ABB IRB 140 industrial robot, first by analyzing the motion profile of only one joint and then by estimating the path tracking of two welding tasks: one rectangular and another one circular. Results from this work prove that the sensor fusion technique accompanied by proper suppression of vibrations delivers better estimation than other proposed techniques.
ISSN:1070-9622
1875-9203
DOI:10.1155/2016/6954012