IEEE Robotics & Automation Magazine - December 2022 - 113

E
E
E
E
S
ψ1
W
E
S
ψ2
W
S
ψ3
W
S
camera's FoV under constraints introduced
by the BLF. Comparative experiments
were performed to demonstrate
the aforementioned theoretical findings.
Further work in the industrial secψ4
W
E
E
S
S
ψ5
W
ψ6
W
S
ψ7
W
S
ψ8
W
Figure
9. A humanoid control demonstration of the Sawyer robot. S: shoulder; E: elbow;
W: wrist.
-0.5
Swivel Angle ψ
-1
Acknowledgment
This work was supported in part by the
National Natural Science Foundation
of China under grants 61733004,
62027810, 62003136, and 62103138;
the National Key Research and Development Program of
China number 2021YFB1714700; the Special Funding Support
for the Construction of Innovative Provinces of Hunan
Province under grants 2021GK1010 and 2019GK1010; the
China Postdoctoral Science Foundation numbers BX2021098
and 2021M701155; and Changsha Municipal Natural Science
Foundation kq2014060.
-1.5
02468
Time (s)
Figure 10. The curve of swivel angle W .
The human-like motion based on the swivel angle is verified
in the Sawyer robot, as shown in Figure 9. To clarify
validity of the inverse kinematics solution with null-space
mapping, the input of the swivel angle is simply set as
()
WW= si (/ ).n
tt4#)r
Figure 10 shows the curve of W ,
and the redundant robot arm can periodically swing around
the axis formed by the shoulder and the wrist according to a
set angle and does not interfere with the execution of the endeffector
task.
Discussion and Conclusions
In this article, swivel-angle motion was introduced to realize
human-like control of an anthropomorphic manipulator. An
IBVS motion controller with constraints and a torque controller
with constraints were designed in combination with a BLF
while ensuring that visual feature coordinates errors respect
FoV constraints. In addition, a sliding mode law with IBVS was
designed to address uncertainty of the visual servoing system
and enhance control performance. The experimental results
illustrate that image features can constantly stay within the
References
[1] Y. Jiang, C. Yang, Y. Wang, Z. Ju, Y. Li, and C.-Y. Su, " Multi-hierarchy
interaction control of a redundant robot using impedance learning, "
Mechatronics, vol. 67, p. 102,348, May 2020, doi: 10.1016/j.mechatronics.2020.102348.
[2]
Y. Jiang, Y. Wang, Z. Miao, J. Na, Z. Zhao, and C. Yang, " Compositelearning-based
adaptive neural control for dual-arm robots with relative
motion, " IEEE Trans. Neural Netw. Learn. Syst., vol. 33, no. 3,
pp. 1010-1021, Mar. 2022, doi: 10.1109/TNNLS.2020.3037795.
[3] L. M. Miller, H. Kim, and J. Rosen, " Redundancy and joint limits of a
seven degree of freedom upper limb exoskeleton, " in Proc. 2011 IEEE
Annu. Int. Conf. Eng. Med., Biol. Soc., pp. 8154-8157, doi: 10.1109/
IEMBS.2011.6092011.
[4] A. M. Zanchettin, L. Bascetta, and P. Rocco, " Acceptability of robotic
manipulators in shared working environments through human-like
redundancy resolution, " Appl. Ergonom., vol. 44, no. 6, pp. 982-989,
2013, doi: 10.1016/j.apergo.2013.03.028.
[5] H. Huang, T. Zhang, C. Yang, and C. P. Chen, " Motor learning and
generalization using broad learning adaptive neural control, " IEEE
Trans. Ind. Electron., vol. 67, no. 10, pp. 8608-8617, 2019, doi: 10.1109/
TIE.2019.2950853.
[6] A. Cherubini, R. Passama, A. Crosnier, A. Lasnier, and P. Fraisse,
" Collaborative manufacturing with physical human-robot interaction, "
Robot. Comput.-Integr. Manuf., vol. 40, pp. 1-13, Aug. 2016, doi:
10.1016/j.rcim.2015.12.007.
[7] H. Su, Y. Hu, H. R. Karimi, A. Knoll, G. Ferrigno, and E. De Momi,
" Improved recurrent neural network-based manipulator control
DECEMBER 2022 * IEEE ROBOTICS & AUTOMATION MAGAZINE *
113
tor will demand more refined and
intelligent manipulations for more
flexible and diversified operation
tasks. Instead of kinematic control,
human-robot impedance control can
be utilized to intensify the safety and
flexibility of HRI. More sensors, such
as force sensors, can be combined
with vision to improve the intelligence
of robot manipulators.
ψ (rad)

http://dx.doi.org/10.1016/j.mechatronics.2020.102348 http://dx.doi.org/10.1016/j.mechatronics.2020.102348 http://dx.doi.org/10.1016/j.apergo.2013.03.028 http://dx.doi.org/10.1109/TIE.2019.2950853 http://dx.doi.org/10.1109/TIE.2019.2950853 http://dx.doi.org/10.1016/j.rcim.2015.12.007

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