RESEARCH ARTICLE

Development of a novel hand−eye calibration for intuitive control of minimally invasive surgical robot

  • Yanwen SUN ,
  • Bo PAN ,
  • Yili FU
Expand
  • State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150001, China

Received date: 01 Dec 2021

Accepted date: 21 Apr 2022

Published date: 15 Sep 2022

Copyright

2022 Higher Education Press

Abstract

Robotic-assisted surgical system has introduced a powerful platform through dexterous instrument and hand−eye coordination intuitive control. The knowledge of laparoscopic vision is a crucial piece of information for robot-assisted minimally invasive surgery focusing on improved surgical outcomes. Obtaining the transformation with respect to the laparoscope and robot slave arm frames using hand−eye calibration is essential, which is a key component for developing intuitive control algorithm. We proposed a novel two-step modified dual quaternion for hand−eye calibration in this study. The dual quaternion was exploited to solve the hand−eye calibration simultaneously and powered by an iteratively separate solution. The obtained hand−eye calibration result was applied to the intuitive control by using the hand−eye coordination criterion. Promising simulations and experimental studies were conducted to evaluate the proposed method on our surgical robot system. We extensively compared the proposed method with state-of-the-art methods. Results demonstrate this method can improve the calibration accuracy. The effectiveness of the intuitive control algorithm was quantitatively evaluated, and an improved hand−eye calibration method was developed. The relationship between laparoscope and robot kinematics can be established for intuitive control.

Cite this article

Yanwen SUN , Bo PAN , Yili FU . Development of a novel hand−eye calibration for intuitive control of minimally invasive surgical robot[J]. Frontiers of Mechanical Engineering, 2022 , 17(3) : 42 . DOI: 10.1007/s11465-022-0698-y

Nomenclature

A Matrix of laparoscope motion
B Matrix of robot motion
K Matrix for solving hand‒eye equation
L Modified matrix for hand‒eye equation solution
M Matrix vector
N Number of experimental test data groups
PBL Robot laparoscope slave arm base frame
PE Robot laparoscope slave arm end-effector frame
PL Laparoscope frame
PO Calibration object frame
in sbase P Translation vector from surgical robot slave instrument arm base frame to the instrument frame
inseye Pi Position of surgical instrument in the surgeon eye frame in time step i
insimg Pi Instrument position with respective with the laparoscope camera image frame
h an deye Pi Position of surgeon hand in the surgeon eye frame in time step i
h an dmas P Translation vector from master system base frame to the surgeon hand frame
qA, qX, qB Dual quaternion representations of Eq. (2)
qAR, qXR, qBR Rotation components
qAR0, qBR0 Components of the qA R and qB R, respectively
qAt, qXt, qBt Translation components
b as elap R Rotation matrix component of baselap T
h an dmas R Rotation matrix component of homogeneous transformation matrix from master system base frame to the surgeon hand frame
in sbase R Rotation matrix component of homogeneous transformation matrix from surgical robot slave instrument arm base frame to the instrument frame
lapimg R Rotation matrix component of homogeneous transformation matrix from laparoscope camera image frame to robot laparoscope slave arm end tool frame
m aseye R Rotation matrix component of maseye T
tA, tB, tX Translation component of the homogeneous transformation matrix in AX = XB
tGT Ground-truth value
b as elap T Transformation matrix from robot laparoscope end tool frame to robot instrument slave arm base frame
ELT Transformation between P L and P E
h an deye T Homogeneous transformation from surgeon eye frame to surgeon hand frame
h an dmas T Homogeneous transformation matrix from master system base frame to the surgeon hand frame
i mgeye T Transformation matrix from surgeon eye frame to the laparoscope camera image frame
in sbase T Homogeneous transformation matrix from surgical robot slave instrument arm base frame to the instrument frame
inseye T Homogeneous transformation from surgeon eye frame to instrument frame
insimg T Homogeneous transformation from laparoscope camera image frame to instrument frame
l apimg T Transformation matrix from laparoscope camera image frame to robot laparoscope slave arm end tool frame
m aseye T Homogeneous transformation from surgeon eye frame to master system base frame
B LET(i) Motion i of transformation between robot laparoscope slave arm base frame P BL and the robot laparoscope slave arm end-effector frame P E
OLT(i) Motion i of transformation between calibration object frame P O and the laparoscope frame P L
X Target homogeneous transformation matrix
ωA, ωB, ωX Rotation component of the homogeneous transformation matrix in AX = XB
ω GT Ground-truth value
γ1, γ 2 Parameters for dual quaternion solution
τ, υ Parameters for dual quaternion solution of Eq. (9)
κ Defined ratio variable for solving equation
αA, βA, αB, βB Lie group members of the transformation
λ Master–slave position mapping scale factor
Δ r Rotation matrix error
Δ t Translation vector error
θt Translation error between calculated value and ground-truth value
θω Rotation error between calculated value and ground-truth value

Acknowledgements

The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. This study was supported by the State Key Laboratory of Robotics and Systems, China (Grant No. SKLRS202009B).
1
Zhong F X, Wang Z R, Chen W, He K J, Wang Y Q, Liu Y H. Hand-eye calibration of surgical instrument for robotic surgery using interactive manipulation. IEEE Robotics and Automation Letters, 2020, 5(2): 1540–1547

DOI

2
Gao Y Q, Wang S X, Li J M, Li A M, Liu H B, Xing Y. Modeling and evaluation of hand‒eye coordination of surgical robotic system on task performance. The International Journal of Medical Robotics and Computer Assisted Surgery, 2017, 13(4): e1829

DOI

3
Su H, Hu Y B, Karimi H R, Knoll A, Ferrigno G, Momi E D. Improved recurrent neural network-based manipulator control with remote center of motion constraints: experimental results. Neural Networks, 2020, 131: 291–299

DOI

4
Zhang W, Li H Y, Cui L L, Li H Y, Zhang X Y, Fang S X, Zhang Q J. Research progress and development trend of surgical robot and surgical instrument arm. The International Journal of Medical Robotics and Computer Assisted Surgery, 2021, 17(5): e2309

DOI

5
Zhang Z Q, Zhang L, Yang G Z. A computationally efficient method for hand–eye calibration. International Journal of Computer Assisted Radiology and Surgery, 2017, 12(10): 1775–1787

DOI

6
Su H, Qi W, Yang C G, Sandoval J, Ferrigno G, Momi E D. Deep neural network approach in robot tool dynamics identification for bilateral teleoperation. IEEE Robotics and Automation Letters, 2020, 5(2): 2943–2949

DOI

7
Wang Z Y, Zi B, Ding H F, You W, Yu L T. Hybrid grey prediction model-based autotracking algorithm for the laparoscopic visual window of surgical robot. Mechanism and Machine Theory, 2018, 123: 107–123

DOI

8
Allan M, Ourselin S, Hawkes D J, Kelly J D, Stoyanov D. 3-D pose estimation of articulated instruments in robotic minimally invasive surgery. IEEE Transactions on Medical Imaging, 2018, 37(5): 1204–1213

DOI

9
Kassahun Y, Yu B B, Tibebu A T, Stoyanov D, Giannarou S, Metzen J H, Vander Poorten E. Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions. International Journal of Computer Assisted Radiology and Surgery, 2016, 11(4): 553–568

DOI

10
Du X F, Kurmann T, Chang P L, Allan M, Ourselin S, Sznitman R, Kelly J D, Stoyanov D. Articulated multi-instrument 2-D pose estimation using fully convolutional networks. IEEE Transactions on Medical Imaging, 2018, 37(5): 1276–1287

DOI

11
Wang Z R, Liu Z W, Ma Q L, Cheng A, Liu Y H, Kim S, Deguet A, Reiter A, Kazanzides P, Taylor R H. Vision-based calibration of dual RCM-based robot arms in human‒robot collaborative minimally invasive surgery. IEEE Robotics and Automation Letters, 2018, 3(2): 672–679

DOI

12
Tsai R Y, Lenz R K. A new technique for fully autonomous and efficient 3D robotics hand/eye calibration. IEEE Transactions on Robotics and Automation, 1989, 5(3): 345–358

DOI

13
Shiu Y C, Ahmad S. Calibration of wrist-mounted robotic sensors by solving homogeneous transform equations of the form AX = XB. IEEE Transactions on Robotics and Automation, 1989, 5(1): 16–29

DOI

14
Chou J C K, Kamel M. Finding the position and orientation of a sensor on a robot manipulator using quaternions. The International Journal of Robotics Research, 1991, 10(3): 240–254

DOI

15
Horaud R, Dornaika F. Hand‒eye calibration. The International Journal of Robotics Research, 1995, 14(3): 195–210

DOI

16
Park F C, Martin B J. Robot sensor calibration: solving AX = XB on the Euclidean group. IEEE Transactions on Robotics and Automation, 1994, 10(5): 717–721

DOI

17
Daniilidis K. Hand‒eye calibration using dual quaternions. The International Journal of Robotics Research, 1999, 18(3): 286–298

DOI

18
Lu Y C, Chou J C K. Eight-space quaternion approach for robotic hand‒eye calibration. In: Proceedings of 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century. Vancouver: IEEE, 1995, 3316–3321

DOI

19
Zhao Z J, Liu Y C. A hand‒eye calibration algorithm based on screw motions. Robotica, 2009, 27(2): 217–223

DOI

20
Li W, Dong M L, Lu N G, Lou X P, Sun P. Simultaneous robot–world and hand–eye calibration without a calibration object. Sensors, 2018, 18(11): 3949

DOI

21
Andreff N, Horaud R, Espiau B. On-line hand–eye calibration. In: Proceedings of the Second International Conference on 3-D Digital Imaging and Modeling. Ottawa: IEEE, 1999, 430–436

DOI

22
Pachtrachai K, Vasconcelos F, Dwyer G, Hailes S, Stoyanov D. Hand‒eye calibration with a remote centre of motion. IEEE Robotics and Automation Letters, 2019, 4(4): 3121–3128

DOI

23
Mao J F, Huang X P, Jiang L. A flexible solution to AX = XB for robot hand‒eye calibration. In: Proceedings of the 10th WSEAS International Conference on Robotics, Control and Manufacturing Technology. Hangzhou: World Scientific and Engineering Academy and Society (WSEAS), 2010, 118–122

DOI

24
Schmidt J, Vogt F, Niemann H. Robust hand–eye calibration of an endoscopic surgery robot using dual quaternions. In: Michaelis B, Krell G, eds. Pattern Recognition. DAGM 2003. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2003, 548–556

DOI

25
Zhao Z J. Hand‒eye calibration using convex optimization. In: Proceedings of 2011 IEEE International Conference on Robotics and Automation (ICRA). Shanghai: IEEE, 2011, 2947–2952

DOI

26
Enebuse I, Foo M, Ibrahim B S K K, Ahmed H, Supmak F, Eyobu O S. A comparative review of hand‒eye calibration techniques for vision guided robots. IEEE Access, 2021, 9: 113143–113155

DOI

27
Pachtrachai K, Vasconcelos F, Edwards P, Stoyanov D. Learning to calibrate—estimating the hand‒eye transformation without calibration objects. IEEE Robotics and Automation Letters, 2021, 6(4): 7309–7316

DOI

28
Pachtrachai K, Allan M, Pawar V, Hailes S, Stoyanov D. Hand‒eye calibration for robotic assisted minimally invasive surgery without a calibration object. In: Proceedings of 2016 IEEE/ RSJ International Conference on Intelligent Robots and Systems (IROS). Daejeon: IEEE, 2016, 2485–2491

DOI

29
Thompson S, Stoyanov D, Schneider C, Gurusamy K, Ourselin S, Davidson B, Hawkes D, Clarkson M J. Hand–eye calibration for rigid laparoscopes using an invariant point. International Journal of Computer Assisted Radiology and Surgery, 2016, 11(6): 1071–1080

DOI

30
Pachtrachai K, Vasconcelos F, Chadebecq F, Allan M, Hailes S, Pawar V, Stoyanov D. Adjoint transformation algorithm for hand–eye calibration with applications in robotic assisted surgery. Annals of Biomedical Engineering, 2018, 46(10): 1606–1620

DOI

31
Su H, Li S, Manivannan J, Bascetta L, Ferrigno G, Momi E D. Manipulability optimization control of a serial redundant robot for robot-assisted minimally invasive surgery. In: Proceedings of 2019 International Conference on Robotics and Automation (ICRA). Montreal: IEEE, 2019, 1323–1328

DOI

32
Morgan I, Jayarathne U, Rankin A, Peters T M, Chen E C S. Hand‒eye calibration for surgical cameras: a procrustean perspective-n-point solution. International Journal of Computer Assisted Radiology and Surgery, 2017, 12(7): 1141–1149

DOI

33
Malti A, Barreto J P. Hand–eye and radial distortion calibration for rigid endoscopes. The International Journal of Medical Robotics and Computer Assisted Surgery, 2013, 9(4): 441–454

DOI

34
Peng J Q, Xu W F, Wang F X, Han Y, Liang B. A hybrid hand–eye calibration method for multilink cable-driven hyper-redundant manipulators. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1–13

DOI

35
Niu G J, Pan B, Ai Y, Fu Y L. Intuitive control algorithm of a novel minimally invasive surgical robot. Computer Assisted Surgery, 2016, 21(sup1): 92–101

DOI

36
Niu G J, Pan B, Fu Y L, Qu C C. Development of a new medical robot system for minimally invasive surgery. IEEE Access, 2020, 8: 144136–144155

DOI

37
Zuo S Y, Wang Z, Zhang T C, Chen B J. A novel master–slave intraocular surgical robot with force feedback. The International Journal of Medical Robotics and Computer Assisted Surgery, 2021, 17(4): e2267

DOI

38
Dimitrakakis E, Lindenroth L, Dwyer G, Aylmore H, Dorward N L, Marcus H J, Stoyanov D. An intuitive surgical handle design for robotic neurosurgery. International Journal of Computer Assisted Radiology and Surgery, 2021, 16(7): 1131–1139

DOI

39
Zhang Z. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330–1334

DOI

Outlines

/