Payload Identification and Gravity/Inertial Compensation for Six-Dimensional Force/Torque Sensor with a Fast and Robust Trajectory Design Approach

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 2; p. 439
• Main Authors: Duan, Jinjun, Liu, Zhouchi, Bin, Yiming, Cui, Kunkun, Dai, Zhendong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2022; MDPI
• Subjects: Algorithms; Collaboration; Contact force; Control algorithms; Excitation; excitation trajectory; fast identification; Fourier series; Genetic algorithms; gravity compensation; inertial compensation; Load; Optimization; Parameter identification; Robots; Sensors; Torque sensors (robotics); inertial compensation; excitation trajectory; fast identification; gravity compensation; Fourier series
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22020439

In the robot contact operation, the robot relies on the multi-dimensional force/torque sensor installed at the end to sense the external contact force. When the effective load and speed of the robot are large, the gravity/inertial force generated by it will have a non-negligible impact on the output of the force sensor, which will seriously affect the accuracy and effect of the force control. The existing identification algorithm time is often longer, which also affects the efficiency of force control operations. In this paper, a self-developed multi-dimensional force sensor with integrated gravity/inertial force sensing function is used to directly measure the resultant force. Further, a method for the rapid identification of payload based on excitation trajectory is proposed. Firstly, both a gravity compensation algorithm and an inertial force compensation algorithm are introduced. Secondly, the optimal spatial recognition pose based on the excitation trajectory was designed, and the excitation trajectory of each joint is represented by a finite Fourier series. The least square method is used to calculate the identification parameters of the load, the gravity, and inertial force. Finally, the experiment was verified on the robot. The experimental results show that the algorithm can quickly identify the payload, and it is faster and more accurate than other algorithms.