Calibration and decoupling of multi-axis robotic Force/Moment sensors
| Autor: | Qiaokang Liang, Gianmarc Coppola, Yaonan Wang, Yunjian Ge, Dan Zhang, Wanneng Wu, Wei Sun |
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| Rok vydání: | 2018 |
| Předmět: |
Engineering
Artificial neural network business.industry General Mathematics Multi axis 010401 analytical chemistry Work (physics) 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences Industrial and Manufacturing Engineering 0104 chemical sciences Computer Science Applications Moment (mathematics) Control and Systems Engineering Control theory Calibration 0210 nano-technology business Software Decoupling (electronics) Simulation Extreme learning machine |
| Zdroj: | Robotics and Computer-Integrated Manufacturing. 49:301-308 |
| ISSN: | 0736-5845 |
| Popis: | Multi-axis robotic Force/Moment (F/M) sensors are capable of simultaneously detecting multiple components of force (Fx, Fy, and Fz), as well as the moments (Mx, My and Mz). This enables them to be frequently used in many robotic applications. Accurate, time-effective calibration and decoupling procedures are critical to the implementation of these sensors. This paper compares the effectiveness of decoupling methods based on Least-Squares (LS), BP Neural Network (BPNN), and Extreme Learning Machine (ELM) methods for improving the performance of multi-axis robotic F/M sensors. In order to demonstrate the effectiveness of the decoupling methods, a calibration and decoupling experiment was performed on a five-axis robotic F/M sensor. The experiments demonstrate that the ELM based decoupling method is superior to LS and BPNN based methods. The presented theoretical and experimental demonstrations provide a comprehensive description of the calibration and decoupling procedures of multi-axis robotic F/M sensors. This work reveals that the ELM method is an appropriate and high performing decoupling procedure for multi-axis robotic F/M sensors. |
| Databáze: | OpenAIRE |
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