Hysteresis Compensation in Force/Torque Sensors Using Time Series Information
Ryuichiro Koike1, Sho Sakaino2, Toshiaki Tsuji3
1Graduate School of Science and Engineering, Saitama University, Sakura-ku, Saitama City, Saitama 338-8570, Japan. koiryu1221@gmail.com.
Sensors (Basel, Switzerland)
|October 3, 2019
Summary
This study compensates for six-axis force sensor hysteresis using machine learning and time series data. Incorporating historical data significantly improves the precision of force sensing calibration.
Area of Science:
- Robotics and Control Systems
- Machine Learning Applications
- Sensor Technology
Background:
- Hysteresis in six-axis force sensors complicates high-precision force sensing.
- Existing one-axis hysteresis models are difficult to extend to multi-axis systems.
- Advanced modeling techniques are needed for accurate multi-axis force sensing.
Purpose of the Study:
- To compensate for hysteresis in six-axis force sensors via signal processing.
- To achieve high-precision force sensing by addressing multi-axis hysteresis challenges.
- To explore machine learning for multi-axis hysteresis compensation.
Main Methods:
- Utilizing machine learning, specifically regression, to model and compensate for hysteresis.
- Investigating the impact of time series data on machine learning model performance.
- Applying signal processing techniques for sensor calibration.
Main Results:
- Machine learning effectively compensates for hysteresis in six-axis force sensors.
- Including time series information in the regression process enhances performance.
- Improved accuracy in force sensing calibration was demonstrated.
Conclusions:
- Machine learning, particularly with time series data, offers a viable solution for multi-axis hysteresis compensation.
- This approach enhances the precision of six-axis force sensors.
- The findings contribute to advancements in high-precision force sensing technology.
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