IEEE Robotics & Automation Magazine - December 2022 - 34

3.5
25%-75%
1
Range Within 1.5 IQR
Median Line
Mean
2.5
3
0.5
1.5
2
VF
Proposed
Figure 8. The compared performance between the VF method
and our proposed method. IQR: interquartile range.
Table 1. Average values of external torque xr
under VF method and our proposed method.
Human Subject
Average Value
VF (Nm)
Proposed (Nm)
1
2
3
0.269 0.324 0.380
0.221 0.184 0.126
Table 2. MSEs under the VP method and our
proposed method.
Human Subject
MSE
VP (rad2)
Proposed (rad2)
1
2
1.318
3
1.836 1.626 2.029
0.902
0.906
1
0.5
-0.5
-1.5
-1
-2
246 10
VP
Proposed Method
Figure 9. External torques with the VP method and our proposed
method.
34 * IEEE ROBOTICS & AUTOMATION MAGAZINE * DECEMBER 2022
81214 16 18 20
Time (s)
Conclusion
In this article, we proposed a hybrid visual-force framework
in a human-robot cotransporting task. A motion prediction
method was proposed to predict human motion for reducing
delays to achieve motion synchronization. Visual sensing was
utilized in capturing human motion in real time, and force
sensing was employed to regulate interaction forces to
improve compliance when a motion error between human
and robot existed. An adaptive NN-based control was developed
to compensate for system uncertainties and achieve precise
tracking. In the experiments, human subjects and a
Baxter robot cotransported a wooden stick from a starting
position to a target position. Comparative experimental
results showed that our proposed framework was effective in
this cotransporting task, and motion synchronization could
be achieved, while the human subjects found it easier to
accomplish the task.
Acknowledgment
This work was supported in part by the National Natural
Science Foundation of China under Grant 62225304, Grant
62061160371, and Grant 62003032; in part by the Beijing
Natural Science Foundation under Grant JQ20026; in part
by the China Postdoctoral Science Foundation under Grant
2020TQ0031 and Grant 2021M690358; in part by the Beijing
Top Discipline for Artificial Intelligent Science and
Engineering, University of Science and Technology Beijing;
in part by the Technological Innovation Foundation of
Shunde Graduate School of University of Science and Technology
Beijing under Grant BK20BE013; and in part by the
Guangdong Basic and Applied Basic Research Foundation
under Grant 2020B1515120071. The corresponding author
is Wei He.
0.5
1
VVFVP Proposed
Figure 10. MSEs of V method, VF method, VP method, and our
proposed method.
under four methods and the averaged MSE of three subjects
under different methods are utilized to evaluate the
performance. We can see that the motion errors under our
proposed method are smaller.
3.019
1.883
1.830
1.042
Torque (Nm)
Torque (Nm)
MSE

IEEE Robotics & Automation Magazine - December 2022

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