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

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.