In this paper, a novel robust online model predictive control (RMPC) method for image-based visual servoing (IBVS) in polar coordinates is proposed. First, the Jacobian matrix is transformed into a weighted combination of vertex matrices of convex polytopic by tensor product (TP) model transformation method. Then, a new IBVS control design condition for 6-DOF manipulator submitting to robot physical limitations and visibility constraints is obtained in polar coordinates by using RMPC technique. The optimal value of the control signal can be solved online when carrying out the convex optimization problem. The proposed control strategy can effectively improve the trajectory in the case that involves translation and rotation with fast response while averting the pseudo-inverse of the image Jacobian matrix. Conclusively, the effectiveness of the proposed scheme is validated by simulations and experiments on a 6-DOF manipulator.
AllibertGCourtialE (2012) Switching controller for efficient IBVS. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp.1695–1701.
2.
AllibertGCourtialEChaumetteF (2010) Predictive control for constrained image-based visual servoing. IEEE Transactions on Robotics26(5): 933–939.
3.
BaranyiPTikkDYamY, et al. (2003) From differential equations to PDC controller design via numerical transformation. Computers in Industry51(3): 281–297.
4.
BaranyiPYamYVárlakiP (2013) Tensor Product Model Transformation in Polytopic Model-based Control. Boca Raton: CRC Press.
5.
CaiCDean-LeónEMendozaD, et al. (2013) Uncalibrated 3d stereo image-based dynamic visual servoing for robot manipulators. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp.63–70.
6.
CaiCSomaniNKnollA (2016) Orthogonal image features for visual servoing of a 6-DOF manipulator with uncalibrated stereo cameras. IEEE Transactions on Robotics32(2): 452–461.
CorkePISpindlerFChaumetteF (2009) Combining Cartesian and polar coordinates in IBVS. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp.5962–5967.
10.
GaoJAnXProctorA, et al. (2017) Sliding mode adaptive neural network control for hybrid visual servoing of underwater vehicles. Ocean Engineering142: 666–675.
11.
GaoJProctorABradleyC (2015) Adaptive neural network visual servo control for dynamic positioning of underwater vehicles. Neurocomputing167: 604–613.
12.
HajilooAKeshmiriMXieW, et al. (2016) Robust online model predictive control for a constrained image-based visual servoing. IEEE Transactions on Industrial Electronics63(4): 2242–2250.
13.
HaoMSunZFujiiM (2007) Image based visual servoing using takagi-sugeno fuzzy neural network controller. In: 2007 IEEE 22nd International Symposium on Intelligent Control. IEEE, pp.53–58.
14.
HashimotoKNoritsuguT (1998) Performance and sensitivity in visual servoing. In: Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146), vol. 3. IEEE, pp.2321–2326.
15.
IslamSLiuPXSaddikAE (2015) Robust control of four-rotor unmanned aerial vehicle with disturbance uncertainty. IEEE Transactions on Industrial Electronics62(3): 1563–1571.
16.
IwatsukiMOkiyamaN (2005) A new formulation of visual servoing based on cylindrical coordinate system. IEEE Transactions on Robotics21(2): 266–273.
17.
Janabi-SharifiFDengLWilsonWJ (2011) Comparison of basic visual servoing methods. IEEE/ASME Transactions on Mechatronics16(5): 967–983.
18.
KeFLiZXiaoH, et al. (2017) Visual servoing of constrained mobile robots based on model predictive control. IEEE Transactions on Systems, Man, and Cybernetics: Systems47(7): 1428–1438.
19.
KeshmiriMXieWFMohebbiA (2014) Augmented image-based visual servoing of a manipulator using acceleration command. IEEE Transactions on Industrial Electronics61(10): 5444–5452.
20.
KothareMVBalakrishnanVMorariM (1996) Robust constrained model predictive control using linear matrix inequalities. Automatica32(10): 1361–1379.
21.
LiTZhaoH (2017) Global finite-time adaptive control for uncalibrated robot manipulator based on visual servoing. ISA transactions68: 402–411.
22.
LuYArkunY (2000) Quasi-min-max MPC algorithms for LPV systems. Automatica36(4): 527–540.
23.
ParkDHKwonJHHaIJ (2012) Novel position-based visual servoing approach to robust global stability under field-of-view constraint. IEEE Transactions on Industrial Electronics59(12): 4735–4752.
24.
TahriOAraujoHChaumetteF, et al. (2013) Robust image-based visual servoing using invariant visual information. Robotics and Autonomous Systems61(12): 1588–1600.
25.
ThomasJLoiannoGDaniilidisK, et al. (2016) Visual servoing of quadrotors for perching by hanging from cylindrical objects. IEEE Robotics and Automation Letters1(1): 57–64.
26.
ThuilotBMartinetPCordessesL, et al. (2002) Position based visual servoing: keeping the object in the field of vision. In: Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No. 02CH37292), vol. 2. IEEE, pp.1624–1629.
27.
WangTXieWLiuG, et al. (2015) Quasi-min-max model predictive control for image-based visual servoing with tensor product model transformation. Asian Journal of Control17(2): 402–416.
28.
WangYLangHde SilvaCW (2009) A robust mobile robot manipulation system using a switch-based visual-servo controller. In: 2009 International Conference on Industrial Mechatronics and Automation. IEEE, pp.364–367.
29.
XieWFLiZTuXW, et al. (2009) Switching control of image-based visual servoing with laser pointer in robotic manufacturing systems. IEEE Transactions on Industrial Electronics56(2): 520–529.
30.
YeGLiWWanH, et al. (2015) Novel two-stage hybrid IBVS controller combining Cartesian and polar based methods. In: 2015 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, pp.397–402.
31.
YükselT (2017) Intelligent visual servoing with extreme learning machine and fuzzy logic. Expert Systems with Applications72: 344–356.
32.
ZhangKChenJLiY, et al. (2018) Unified visual servoing tracking and regulation of wheeled mobile robots with an uncalibrated camera. IEEE/ASME Transactions on Mechatronics23(4): 1728–1739.
33.
ZhangYLiSLiaoB, et al. (2017) A recurrent neural network approach for visual servoing of manipulators. In: 2017 IEEE International Conference on Information and Automation (ICIA). IEEE, pp.614–619.
