Visual Servo Control of Robot Grasping

Part of the Mechanisms and Machine Science book series (Mechan. Machine Science, volume 10)


This chapter describes the basic principles and methods for visual servo control of robot manipulators in grasping tasks, proposing implementing solutions with multitasking robot-vision controllers. Guidance vision is introduced as an advanced motion control method, which provides flexibility when integrating robots in manufacturing cells with unstructured environment and in line quality inspection. Two important system architectures are analyzed: dynamic look-and-move systems (open loop robot-vision architectures), and direct visual servoing systems (closed-loop robot-vision architectures). The generic task analyzed in this chapter is the visual tracking of material flows with fixed and mobile cameras for robot grasping from stationary and moving scenes. Novel contributions are included with respect to modeling the grasping style, robot guidance from mobile, arm-mounted cameras, and authorizing collision-free object grasping based on real-time fingerprint evaluation.


Robot grasping control Guidance vision Visual servoing EOL ECL 


  1. 1.
    Feddema J, Lee CSG, Mitchell OR (1991) Weighted selection of image features for resolved rate visual feedback control. IEEE Trans Robot Autom 7(February):31–47CrossRefGoogle Scholar
  2. 2.
    Borangiu TH (2002) Advanced robot motion control. Romanian Academy Press & AGIR Press, BucharestGoogle Scholar
  3. 3.
    Allen PK, Timcenko A, Yoshimi B, Michelman P (1993) Automated tracking and grasping of a moving object with a robotic hand-eye system. IEEE Trans. Robot. Automat. 9(2):152–165CrossRefGoogle Scholar
  4. 4.
    Borangiu TH, Ionescu F, Manu M (2003) Visual servoing in robot motion control. In: Proceedings of 7th multi-conference on systemics, cybernetics and informatics SCI’03, Orlando, July 27–30, pp 987–992Google Scholar
  5. 5.
    Borangiu TH, Kopacek P (eds) (2004) Proceedings volume from the IFAC workshop intelligent assembly and disassembly-IAD’03, Bucharest, Oct 9–11, 2003, Elsevier Science, OxfordGoogle Scholar
  6. 6.
    Arimoto S, Naniva T, Parra-Vega V, Whitcomb LL (1995) A class of Quasi-neutral potentials for robot servo–loops and its role in adaptive and learning controls, intelligent automation and soft computing, 1, 1, AutoSoft Press, 85–98Google Scholar
  7. 7.
    Battilotti S, Lanari L (1996) Tracking with disturbance attenuation for rigid robots. In: Proceedings of IEEE international conference on robot automation, Minneapolis, pp 1570–1583, April 1996Google Scholar
  8. 8.
    Borangiu TH, Dupas M (2001) Robot–vision. Mise en œuvre en V+. Romanian Academy Press & AGIR Press, BucharestGoogle Scholar
  9. 9.
    Borangiu TH (2001) Reactive control architecture for mobile robots. In: Proceedings of the 2nd national workshop on mobile robots, Craiova, pp 47–52 (2001)Google Scholar
  10. 10.
    Borangiu TH, Ecaterina Oltean V, Manu M (2000) Multi-processor design of nonlinear robust motion control for rigid robots, Lecture Notes in Computer Science No. 1798, Springer, Berlin, pp 224–238Google Scholar
  11. 11.
    Borangiu TH, Ivanescu NA (2003) Visual servo architectures for rigid robots. In: Proceedingts of the 12th international workshop on robotics in Alpe-Adria-Danube Region RAAD’03, Cassino, May 7–10, pp 221–226 Google Scholar
  12. 12.
    Doulgeri Z, Fahantidis N, Konstandinis A (1998) On the decoupling of position and force controllers in constrained robotic tasks. J Robot Syst 15(6):373–340CrossRefGoogle Scholar
  13. 13.
    Corke P (2001) Robotics toolbox release 6,, MathWorks FTP Server, April 2001Google Scholar
  14. 14.
    Freeman R, Kokotovic P (1996) Lyapunov design. In: Levine WS (ed) The control handbook. CRC Press, New York, pp 932–940Google Scholar
  15. 15.
    Haralick RM, Shapiro LG (1993) Computer and robot vision. Boston, Addison-WesleyGoogle Scholar
  16. 16.
    Hoffbeck JP, Langrebe DA (1996) Covariance matrix estimation and classification with limited training data. IEEE Trans Pattern Anal Mach Intell 8(11):431–442Google Scholar
  17. 17.
    Bejczy AK, Kim WS, Venerna SC (1990) The phantom robot: predictive displays for tele-operation with time delay. In: Proceedings of IEEE international conference on robotics and automation, pp 546–551, 1990Google Scholar
  18. 18.
    Jang BK, Chin RT (1992) One-pass parallel thinning: analysis, properties and quantitative evaluation. IEEE Trans Pattern Anal Mach Intell 14:11CrossRefGoogle Scholar
  19. 19.
    Mc Carragher B, Horland G, Sikka P, Aigner P, Austin D (1997) Hybrid dynamic modelling and control of constrained manipulator systems. IEEE Robot Autom Mag 4(2):27–44Google Scholar
  20. 20.
    Nelson BJ, Papanikolopoulos NP, Khosla PK (June 1996) Robotic visual servoing and robotic assembly tasks. IEEE Robot Autom Mag 23:97–102Google Scholar
  21. 21.
    Hosoda K, Asada M (1994) Versatile visual servoing without knowledge of true Jacobean. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systems IROS’94, New York, pp 186–191, Sept 1994Google Scholar
  22. 22.
    Ştefănoiu D, Borangiu TH, Ionescu F (2003) Generating planar workspace boundaries by radius parsimonious increase, preprints of the IFAC interantional workshop intelligent assembly and disassembly IAD’03, Bucharest, Oct 9–11, 90–95 (2003)Google Scholar
  23. 23.
    Borangiu TH, Manu M (1999) Robot-object calibration techniques. In: Proceedings of international conference of IFAC/IEEE control systems and computer science CSCS’99, Bucharest, pp 218–223Google Scholar
  24. 24.
    Borangiu TH, Ivanescu N, Brotac S (2002) An analytical method for visual robot –object calibration. In: Proceedings of the 7th international workshop robotics in Alpe-Adria-Danube Region RAAD’98, Balatonfüred, pp 149–154 Google Scholar
  25. 25.
    Hossu A, Borangiu TH, Croicu AL (1995) Robotvisionpro machine vision software for industrial training and applications. Version 2.2, Cat. #100062, Amsterdam, Tel Aviv, New Jersey, Eshed Robotec (1982), 1995Google Scholar
  26. 26.
    Mamitsvalov AG (1998) n-dimensional moment invariants and conceptual mathematical theory of recognition n-dimensional solids, IEEE Trans Pattern Anal Mach Intell 20:8Google Scholar
  27. 27.
    Moscheni F, Bhattacharjee S, Kunt M (1998) Spatiotemporal segmentation based on region merging, IEEE Trans Pattern Anal Mach Intell 20:8CrossRefGoogle Scholar
  28. 28.
    Nguyen T, Oommen BJ (1997) Moment preserving piecewise linear approximations of signals and images. IEEE Trans Pattern Anal Mach Intell 19:1)Google Scholar
  29. 29.
    Park J, Bien Z (1995) Design of an advanced machine vision system for industrial inspection. Intell Autom Soft Comput 1(2):209–219Google Scholar
  30. 30.
    Pearce A, Caelli T, Bischof WF (1994) Rulegraphs for graph matching in pattern recognition. Pattern Recogn 27:1231–1248CrossRefGoogle Scholar
  31. 31.
    Qi L, Tufto DW (1997) Principal feature classification. IEEE Trans Neural Netw 8(1):167–172Google Scholar
  32. 32.
    Nethery JF, Spong MW (1994) Robotica: a mathematica package for robot analysis. IEEE Robot Autom Mag 1(1):13–20Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Centre of Research in Robotics CIMRUniversity Politehnica of BucharestBucharestRomania

Personalised recommendations