Skip to main content

Crane Payload Position Measurement Vision-Based System Dedicated for Anti-sway Solutions

  • Conference paper

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 471))

Abstract

Handling operation efficiency in cargo transportation realized by the cranes mainly depends on counteractions against undesirable phenomena’s such as payload swing, crane bridge deflections in vertical plane and many others. Although experienced crane operators are an experts in suppressing an excessive payload sway, there is a strong need to develop robust anti-sway systems supporting their work. In this paper authors propose a kind of an absolute payload position measurement system with the use of an image sensor for sway detection. The proposed technique is based on kind of template matching method with the use of a smartcam as a reliable vision sensor. In the described system, vision sensor measures displacement of the markers attached to the cranes hook. On the markers shifts base measure from equilibrium position and actual crane rope length, the payload swing can be estimated. All experiments and tests presented in this paper were conducted on the scaled physical model of overhead travelling crane with hosting capability of 150 kg.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Szpytko, J.: Exploitation testing approaches for large dimensional rails devices. In: Proc. of the 9th IEEE International Conference on Methods and Models in Automation and Robotics MMAR, Poland, pp. 763–768 (2004)

    Google Scholar 

  2. Szpytko, J.: Integrated decision making supporting the exploitation and control of transport devices. UWND AGH, Kraków (2004)

    Google Scholar 

  3. Ziyad, N.M.: Effect of hoisting cable elasticity on anti-sway controllers of quay-side container cranes. Nonlinear Dyn. 58, 129–140 (2009)

    Article  MATH  Google Scholar 

  4. Szpytko, J., Wozniak, D.A.: To keep operational potential of transport device e-based on reliability indicators. In: European Safety and Reliability Conference ESREL, Stavanger, Norway, pp. 2377–2384 (2007)

    Google Scholar 

  5. Sawodny, O., Neupert, J., Arnold, E.: Actual Trends in Crane Automation – Directions for the Future. FME Transactions 37(4), 167–174 (2009)

    Google Scholar 

  6. Smoczek, J.: Evolutionary optimization of interval mathematics-based design of TSK fuzzy controller for anti-sway crane control. International Journal of Applied Mathematics and Computer Science 23(4), 749–759 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  7. Smoczek, J.: Interval arithmetic-based fuzzy discrete-time crane control scheme design. Bulletin of the Polish Academy of Sciences - Technical Sciences 61(4), 863–870 (2013)

    Article  MathSciNet  Google Scholar 

  8. Smoczek, J.: Fuzzy crane control with sensorless payload deflection feedback for vibration reduction. Mechanical Systems and Signal Processing 46(1), 70–81 (2014)

    Article  Google Scholar 

  9. Saeidi, H., Naraghi, M., Abolghasem, A.R.: A neural network self tuner based on input shapers behavior for anti sway system of gantry cranes. Journal of Vibration and Control 19(13), 1936–1949 (2012)

    Article  Google Scholar 

  10. Akira, A.: Anti-sway control for overhead cranes using neural networks. International Jouranla of Innovative Computing, Information and Control 7(7(B)), 4251–4262 (2011)

    Google Scholar 

  11. Mendez, J.A.: An application of a neural self controller to an overhead crane. Neural Computing and Applications 8, 143–150 (1999)

    Article  Google Scholar 

  12. Smoczek, J., Szpytko, J.: Evolutionary algorithm-based design of a fuzzy TBF predictive model and TSK fuzzy anti-sway crane control system. Engineering Applications of Artificial Intelligence 28, 190–200 (2014)

    Article  Google Scholar 

  13. Nakazono, K.: Ohnisihi,t K., Kinjot, H.: Load swing suppression in jib crane systems using a genetic algorithm-trained neuro-controller. In: Proceedings of International Conference on Mechatronics, Kumamoto Japan, pp. 1–4 (2007)

    Google Scholar 

  14. Hyla, P., Szpytko, J.: Vision method for rope angle swing measurement for overhead travelling cranes – validation approach. In: Mikulski, J. (ed.) TST 2013. CCIS, vol. 395, pp. 370–377. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  15. Wang, L., Xi, J. (eds.): Smart devices and machines for advanced manufacturing. Springer, London (2008)

    Google Scholar 

  16. Yu, S., Real, F.D.: Smart Cameras: Fundamentals and classification. In.: Belbachir, A.N. (ed.) Smart Cameras. Springer (2010)

    Google Scholar 

  17. Hornberg, A. (ed.): Handbook of machine vision. Wiley-Vch Verlag, Darmstadt (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hyla, P., Szpytko, J. (2014). Crane Payload Position Measurement Vision-Based System Dedicated for Anti-sway Solutions. In: Mikulski, J. (eds) Telematics - Support for Transport. TST 2014. Communications in Computer and Information Science, vol 471. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45317-9_43

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-45317-9_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45316-2

  • Online ISBN: 978-3-662-45317-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics