Citation: Duan, J.; Liu, Z.; Bin, Y.;
Cui, K.; Dai, Z. Payload Identification
and Gravity/Inertial Compensation
for Six-Dimensional Force/Torque
Sensor with a Fast and Robust
Trajectory Design Approach. Sensors
2022, 22, 439. https://doi.org/
10.3390/s22020439
Academic Editors: Yuansong Qiao
and Seamus Gordon
Received: 17 November 2021
Accepted: 5 January 2022
Published: 7 January 2022
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Article
Payload Identification and Gravity/Inertial Compensation for
Six-Dimensional Force/Torque Sensor with a Fast and Robust
Trajectory Design Approach
Jinjun Duan
1,2,
* , Zhouchi Liu
1
, Yiming Bin
1
, Kunkun Cui
1
and Zhendong Dai
1
1
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics,
Nanjing 210016, China; L16605223643@163.com (Z.L.); bingley@nuaa.edu.cn (Y.B.); kkcui@nuaa.edu.cn (K.C.);
zddai@nuaa.edu.cn (Z.D.)
2
Key Laboratory of Measurement and Control of Complex Systems of Engineering, Southeast University,
Ministry of Education, Nanjing 210096, China
* Correspondence: duan-jinjun@nuaa.edu.cn; Tel.: +86-152-5186-8386
Abstract:
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.
Keywords:
gravity compensation; inertial compensation; excitation trajectory; Fourier series; fast
identification
1. Introduction
In recent years, most industrial/collaborative robots are used in non-contact oper-
ations, such as spraying, polishing, gluing, and handling. With the increasing demand
for small-batch, flexible, and multi-variety operations, the demand for contact operations
such as polishing, polishing, assembly, remote operation, and human–computer interaction
has become increasingly significant. For the robot contact operations, the end-effector
of the robot needs to accurately sense the contact force with the environment to ensure
the smooth progress of the contact operation. There are many ways to sense the contact
force between the end-effector of the robot and the environment: (1) Estimate the wrench
based on the current [
1
]; (2) based on the joint force sensor, the end contact force is sensed
through Jacobian transposition [
2
]; (3) the external contact force is directly sensed through
the six-dimensional force sensor installed on the end-effector [
3
]. If the external contact
force is accurately sensed through the current estimation method, it needs to be based on a
dynamic model, and this method requires dynamic parameter identification and nonlinear
control [
4
]. At present, it is difficult to achieve precise dynamic control by current estima-
tion alone, and external force perception is not feasible. It is not realistic to add a torque
sensor to the existing machine, and it is also difficult to control based on dynamics [5].
Sensors 2022, 22, 439. https://doi.org/10.3390/s22020439 https://www.mdpi.com/journal/sensors
