Robotic Inspection Systems

  • Christian Eitzinger
  • Sebastian Zambal
  • Petra Thanner
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


Industrial quality control often includes the inspection of parts of complex geometry. While such an inspection can be quite easily done by humans, it poses certain challenges if the task is to be automated. Quite often, robots are used for handling the part to acquire a large number of images, each showing a certain area of the surface. The process of acquiring sequences of multiple images also has implications for the machine vision and analysis methods used in such tasks. This chapter covers all topics that relate to the implementation of robotic inspection systems for industrial quality control. The focus is on machine vision, while aspects that deal with robotics will only be addressed at a conceptual level.


Fibre Orientation Inspection System Crack Detection Fibre Angle Inspection Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The work presented in this chapter received cofunding from the European Commission in the 7th Framework Programme, projects “ThermoBot” (No. 284607) and FibreMap (No. 608768), and the Austrian Research Funding Agency (FFG), projects “SelTec”, “ProFit” and “LISP”.

The authors would like to thank all partners of these projects, and especially, Prof. Emanuele Menegatti, Dr. Stefano Ghidoni and the whole team of the IAS Lab of the University of Padova.


  1. 1.
    Tătar O, Mândru D, Ardelean I (2007) Development of mobile minirobots for in pipe inspection tasks. MECHANIKA, 6(68):1392–1207Google Scholar
  2. 2.
    Granosik G, Borenstein J, Hansen MG (2006) Serpentine robots for industrial inspection and surveillance. In: Huat LK (ed) Industrial robotics: programming, simulation and application, pp 702, ISBN: 3 86611-286-6Google Scholar
  3. 3.
    Edinbarough I, Balderas R, Bose S (2005) A vision and robot based on-line inspection monitoring system for electronic manufacturing. Comput Ind 56:986–996CrossRefGoogle Scholar
  4. 4.
    Woern H, Laengle T, Gauss M (2003) ARIKT: adaptive robot based visual inspection. Künstliche Intelligenz 2:33–35Google Scholar
  5. 5.
    Kuhlenkoetter B, Krewet C, Schueppstuhl T (2006) Adaptive robot based reworking system, industrial robotics: programming, simulation and applications. Low Kin Huat (ed), ISBN: 3-86611-286-6Google Scholar
  6. 6.
    Yang CC, Ciarallo FW (2001) Optimized sensor placement for active visual inspection. J Robotic Syst 18(1):1–15MATHCrossRefGoogle Scholar
  7. 7.
    Biegelbauer G, Vincze M, Noehmayer H, Eberst C (2004) Sensor based robotics for fully automated inspection of bores at low volume high variant parts. IEEE international conference on robotics and automation, 5:4852—4857, 26 April–1 May 2004Google Scholar
  8. 8.
    Merat FL, Radack GM (1992) Automatic inspection planning within a feature-based CAD system. Robotics Comput Integr Manuf 9(1):61–66CrossRefGoogle Scholar
  9. 9.
    Park TH, Kim HJ, Kim N (2006) Path planning of automated optical inspection machines for PCB assembly systems. Int J Control Autom Syst 4(1):96–104Google Scholar
  10. 10.
    Scoot WR, Roth G (2003) View planning for automated three-dimensional object reconstruction and inspection. ACM Comput Surv 35(1):64–96CrossRefGoogle Scholar
  11. 11.
    Lee KH, Park HP (2000) Automated inspection planning of free-form shape parts by laser scanning. Robot Comput Integr Manuf 16(4):201–210CrossRefGoogle Scholar
  12. 12.
    Lu CG, Morton D, Wu MH, Myler P (1999) Genetic algorithm modelling and solution of inspection path planning on a coordinate measuring machine (CMM). Int J Adv Manuf Technol 15:409–416CrossRefGoogle Scholar
  13. 13.
    Scott W, Roth G, Rivest JF (2002) Pose error effects on range sensing. In Proceedings of the 15th international conference on vision interface (Calgary, Alta., Canada), pp 331–338Google Scholar
  14. 14.
    Pisinger D, Ropke S (2010) Large neighborhood search. Handbook of Metaheuristics, pp 399–419Google Scholar
  15. 15.
    Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B 26(1):29–41CrossRefGoogle Scholar
  16. 16.
    Ankerl M, Hämmerle A (2009) Applying ant colony optimisation to dynamic pickup and delivery. In: Comput Aided Syst Theory-EUROCAST, pp 721–728Google Scholar
  17. 17.
    Daniilidis K (1999) Hand-eye calibration using dual quaternions. Int J Robot Res 18(3):286–298MathSciNetCrossRefGoogle Scholar
  18. 18.
    Zhao Z, Liu Y (2006) Hand-eye calibration based on screw motions. 18th International conference on pattern recognition (ICPR’06), 3:1022–1026Google Scholar
  19. 19.
    Hollerbach JM, Wampler CW (1996) The calibration index and taxonomy for robot kinematic calibration methods. Int J Robot Res 15(6):573–591CrossRefGoogle Scholar
  20. 20.
    Pradeep V, Konolige K, Berger E (2010) Calibrating a multi-arm multi-sensor robot: a bundle adjustment approach. In: Proceedings of the International symposium on experimental robotics (ISER), Delhi India, Dec 18–21 2010Google Scholar
  21. 21.
    Strobl KH, Hirzinger G (2008) More accurate camera and hand-eye calibrations with unknown grid pattern dimensions. Proceedings-IEEE international conference on robotics and automation, pp 1398–1405Google Scholar
  22. 22.
    Eitzinger C, Heidl W, Lughofer E, Raiser S, Smith JE, Tahir MA, Sannen D, Van Brussel H (2009) Assessment of the influence of adaptive components in trainable surface inspection systems. Mach Vis Appl J, doi:  10.1007/s00138-009-0211-1
  23. 23.
    Smith JE, Tahir MA, Caleb-Solly P, Lughofer E, Eitzinger C, Sannen D, Nuttin M (2009) Human-machine interaction issues in quality control based on on-line image classification. IEEE Trans Syst Man Cybern 39(5):960–971CrossRefGoogle Scholar
  24. 24.
    Eitzinger C, Thumfart S (2012) Optimizing feature calculation in adaptive machine vision systems. In: Sayed-Mouchaweh M, Lughofer E, Learning in non-stationary environments: methods and applications, Springer Science+Business Media New York, doi:  10.1007/978-1-4419-8020-5_13
  25. 25.
    Szeliski R (2006) Image alignment and stitching: a tutorial. Found Trends Comput Graph Vis 2(1):1–104CrossRefGoogle Scholar
  26. 26.
    Brown M, Lowe D (2007) Automatic panoramic image stitching using invariant features. Int J Comput Vision 74(1):59–73CrossRefGoogle Scholar
  27. 27.
    Kopf C, Heindl C, Rooker M, Bauer H, Pichler A (2013) A portable, low-cost 3D body scanning system. 4th International conference and exhibition on 3D body scanning technologies, CA USA Nov 19–20 2013Google Scholar
  28. 28.
    Schmitt R, Mersmann C, Schoenberg A (2009) Machine vision industrialising the textile-based FRP production. In: Proceedings of 6th international symposium on image and signal processing and analysis, pp 260–264Google Scholar
  29. 29.
    Palfinger W, Thumfart S, Eitzinger C (2011) Photometric stereo on carbon fibre surfaces. Proceeding of the Austrian association for pattern recognitionGoogle Scholar
  30. 30.
    Thumfart S, Palfinger W, Stöger M, Eitzinger C (2013) Accurate fibre orientation measurement for carbon fibre surfaces. 15th International conference on computer analysis of images and patterns, pp. 75–82Google Scholar
  31. 31.
    Woodham R (1989) Photometric method for determining surface orientation from multiple images. Opt Eng 19(1):139–144Google Scholar
  32. 32.
    Johnson M, Adelson E (2011) Microgeometry capture using an elastomeric sensor. Comput Vis Pattern Recogn pp 2553–2560Google Scholar
  33. 33.
    Zhou K, Wang L, Tong Y, Desbrun M, Guo B, Shum HY (2005) Texture montage: seamless texturing of arbitrary surfaces from multiple images. Proceedings of ACM SIGGRAPH, pp 1148–1155Google Scholar
  34. 34.
    Avdelidis N, Gan T-H, Ibarra-Castanedo C, Maldaque X (2011) Infrared thermography as a non-destructive tool for martials characterisation and assessment. Proceedings—SPIE the international society for optical engineering, (8013–8039) Thermal Infrared Applications XXXIIIGoogle Scholar
  35. 35.
    Holst G (2000) Common sense approach to thermal imaging. SPIE Volume PM-86, pp 60, ISBN: 0-8194-3722-0Google Scholar
  36. 36.
    Taib S, Jadin M, Kabir S (2012) Thermal Imaging for enhancing inspection reliability: detection and characterization, infrared thermography, Dr. Raghu V Prakash (Ed.), ISBN: 978-953-51-0242-7Google Scholar
  37. 37.
    Traxler G, Thanner P (2011) Automatisierte Wärmeflussprüfungen in der Stahlindustrie, Leitfaden zur Wärmeflussthermografie, ISBN 978-8396-0234-8Google Scholar
  38. 38.
    Ghidoni S, Minella M, Nanni L, Ferrari C, Moro M, Pagello E, Menegatti E (2013) Automatic crack detection in thermal images for metal parts. International conference on heating by electromagnetic sources (HES-13)Google Scholar

Copyright information

© Springer-Verlag London (outside the USA) 2015

Authors and Affiliations

  • Christian Eitzinger
    • 1
  • Sebastian Zambal
    • 1
  • Petra Thanner
    • 1
  1. 1.Profactor GmbHSteyr-GleinkAustria

Personalised recommendations