Abstract
To improve the dynamic characteristic of two-axis force sensors, a dynamic compensation method is proposed. The two-axis force sensor system is assumed to be a first-order system. The operation frequency of the system is expanded by a digital filter with backward difference network. To filter high-frequency noises, a low-pass filter is added after the dynamic compensation network. To avoid overcompensation, parameters of the proposed dynamic compensation method are defined by trial and error. Step response methods are utilized in dynamic calibration experiments. Compared to experiment data without compensation, the response time of the dynamic compensated data is reduced by 30%~40%. Experiments results demonstrate the effectiveness of our method.
Highlights
Multiaxis robot wrist force sensors are necessary for robotic systems in which contact force information between robots and environments needs to be obtained
We proposed a dynamic compensation method based on a digital filter with backward difference design
The two-axis force sensor is assumed as a first-order system
Summary
Multiaxis robot wrist force sensors are necessary for robotic systems in which contact force information between robots and environments needs to be obtained. Multiaxis robot wrist force sensors are always mounted on the wrists of robots to convert multidimensional contact force signals into multichannel voltage signals. Such kinds of applications can be frequently found in assemble robots, teleoperation robotic systems, rehabilitation robots, and so forth [6,7,8,9]. The strong real time and rapidity in robot tasks require multiaxis force sensors to perform high dynamic characteristic. Dynamic compensation algorithms need to be designed to improve dynamic behavior of force sensors. Yu et al in [13] designed dynamic compensation algorithms by genetic neutral network for robot wrist force sensor, but the artificial neural network is slow to converge and may be subjected to local minimum [14]. The experiment results demonstrated the correctness and effectiveness of our method
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