An Integrated Compensation Method for the Force Disturbance of a Six-Axis Force Sensor in Complex Manufacturing Scenarios

• Main Authors: Lei Yao, Qingguang Gao, Dailin Zhang, Wanpeng Zhang, Youping Chen
• Format: Article
• Language: English
• Published: MDPI AG 2021-07-01
• Series: Sensors
• Subjects: robot; six-axis force sensor; deep learning; least squares
• Online Access: https://www.mdpi.com/1424-8220/21/14/4706

As one of the key components for active compliance control and human–robot collaboration, a six-axis force sensor is often used for a robot to obtain contact forces. However, a significant problem is the distortion between the contact forces and the data conveyed by the six-axis force sensor because of its zero drift, system error, and gravity of robot end-effector. To eliminate the above disturbances, an integrated compensation method is proposed, which uses a deep learning network and the least squares method to realize the zero-point prediction and tool load identification, respectively. After that, the proposed method can automatically complete compensation for the six-axis force sensor in complex manufacturing scenarios. Additionally, the experimental results demonstrate that the proposed method can provide effective and robust compensation for force disturbance and achieve high measurement accuracy.