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Multimedia Tools and Applications

, Volume 78, Issue 10, pp 13279–13295 | Cite as

Robust and fast visual tracking for a ball and plate control system: design, implementation and experimental verification

  • Lei ZhengEmail author
  • Renjie Hu
Article
  • 56 Downloads

Abstract

The ball and plate System (BPS) is a two-dimensional electromechanical system with multiple variables, non-linearity and strong coupling. The BPS control problem is to hold the rolling ball in a specific position on the plate by adjusting the plate inclination. Ball tracking is therefore the fundamental step in BPS control, which can largely influence the control effectiveness and efficiency. The segmented path planning based on the sequential thinning algorithm is one popular tracking technology. However, it suffers from high dependence on the operating environment, complex operation and slow speed. This paper innovatively proposes a robust and fast visual tracking solution for BPS. A novel hardware structure has been designed. The sensing camera is rigidly connected to the plate, which avoids the coordinate transformation and thus reduces the complexity. In path recognition, a parallel thinning algorithm is used to improve the processing speed. Additionally, in path planning, a window searching algorithm combining the slope order matching method is proposed to establish the linked list that describes the movement path. A cascaded structure of the BPS tracking controller is also designed. Experiments have shown the effectiveness of the whole system, exhibiting shorter travelling time, smaller tracking errors as well as better stability compared to conventional systems.

Keywords

Ball and plate system Moving object detection Path planning Tracking Control 

Notes

References

  1. 1.
    Bardyn JJ et al (1984) Une architecture vlsi pour un operateur de filtrage median. Congtres Reconnaissance des Forms et Intelligence Artificielle (vol. 1, pp. 557-566), Paris, 25-27Google Scholar
  2. 2.
    Bataineh B, Abdullah SNHS, Omar K (2011) An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows. Pattern Recogn Lett 32(14):1805–1813CrossRefGoogle Scholar
  3. 3.
    Chung KL, Lin HY (1995) Hough transform on reconfigurable meshes. Comput Vis Image Underst 61:278–284CrossRefGoogle Scholar
  4. 4.
    Colmenares SG, Moreno-Armendariz MA, Yu W, Rodriguez FO (2012) Modeling and Nonlinear PD regulation for Ball and Plate System. Conference: World Automation Congress (WAC)Google Scholar
  5. 5.
    Dong YX (2014) Review of Otsu Segmentation Algorithm. Adv Mater Res 989-994:1959–1961CrossRefGoogle Scholar
  6. 6.
    Duda RO, Hart PE (1975) Use of the Hough transformation to detect lines and curves in pictures. Communications of the Association for Computing Machinery 18:120–122CrossRefGoogle Scholar
  7. 7.
    Durus M, Ercil A (2007) Robust vehicle detection algorithm. Signal processing and Communications Applications. SIU 207, IEEE 15th, l-4Google Scholar
  8. 8.
    Fan J, Han M (2012) Nonliear model predictive control of ball-plate system based on gaussian particle swarm optimization. WCCI 2012 IEEE World Congress on Computational Intelligence, BrisbaneCrossRefGoogle Scholar
  9. 9.
    Fan X, Zhang N, Teng S (2004) Trajectory planning and tracking of ball and plate system using hierarchical fuzzy control scheme. Fuzzy Sets Syst 144(2):297–312MathSciNetCrossRefGoogle Scholar
  10. 10.
    Guan-zheng TAN, Xiong XU, Hong-feng XIAO (2005) Real-time and Accurate Hand Path Tracking and Joint Trajectory Planning for Industrial Robots. Journal of Central South University (Science and Technology) 36(1):102–107CrossRefGoogle Scholar
  11. 11.
    Han K-w, Tian Y-t, Kong Y-s, Zhang Y-h, Li J-s (2014) Adaptive Decoupled Sliding Mode Control for the Ball and Plate System. Journal of Jilin University (Engineering and Technology Edition) 44(3):718–725Google Scholar
  12. 12.
    Jain A, Gupta R (2015) Gaussian filter threshold modulation for filtering flat and texture area of an image. Conference Proceeding - 2015 International Conference on Advances in Computer Engineering and Applications, ICACEA, 22:760-763Google Scholar
  13. 13.
    KaewTraKulPong P, Bowden R (2001) An improved adaptive background mixture model for realtime tracking with shadow detection. In Proc. 2nd European Workshop on Advanced Video Based Surveillance Systems, AVBS01. VIDEO BASED SURVEILLANCE SYSTEMS: Computer Vision and Distributed Processing, Kluwer Academic PublishersGoogle Scholar
  14. 14.
    Kimme C, Ballard DH, Sklansky J (1972) Finding circles by an array of accumulators. Communications of the Association for Computing Machinery 15:11–15CrossRefzbMATHGoogle Scholar
  15. 15.
    Lam L, Lee S-W, Suen CY (1992) Thinning Methodologies-A Comprehensive Survey. IEEE Trans Pattern Anal Mach Intell 14(9):869–885CrossRefGoogle Scholar
  16. 16.
    Lee T-C, Kashyap RL, Chu C-N (1994) Building Skeleton Models Via 3-D Medial Surface Axis Thinning Algorithms. Computer Vision, Graphics, and Image Processing 56(6):462–478Google Scholar
  17. 17.
    Li Y, Wang Q, Lin Q, Deng N (2012) On Machine Vision based Ball and Plate Labyrinth System. Proceedings of the 31st Chinese Control Conference, HefeiGoogle Scholar
  18. 18.
    Li LZ et al (2014) A New Approach for Gray Image Segmentation Using Level Set Method. Appl Mech Mater 530-531:372–376CrossRefGoogle Scholar
  19. 19.
    Ma JY, Jie FR, Hu YJ (2017) Moving target detection method based on improved Gaussian mixture model. Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 1042014.  https://doi.org/10.1117/12.2282506
  20. 20.
    Ma Y, Zhu W, Shixia A et al (2007) Improved moving target detection method based on Gaussian Mixture Model. Computer Application 27(10):2544–2548Google Scholar
  21. 21.
    Peng P, Tao Z (2017) Application of an Improved Background Algorithm in SIFT. Journal of Longyan University 35(2)Google Scholar
  22. 22.
    Sang J, Fang Q, Xu C (2017) Exploiting Social-Mobile Information for Location Visualization. ACM Transactions on Intelligent Systems and Technology (TIST) - Special Issue: Mobile Social Multimedia Analytics in the Big Data Era and Regular Papers, 8(3)Google Scholar
  23. 23.
    Sang J, Xu C (2012) Right buddy makes the difference: an early exploration of social relation analysis in multimedia applications. ACM Multimedia, pp. 18-28Google Scholar
  24. 24.
    Sang J, Xu C, Liu J (2012) User-Aware Image Tag Refinement via Ternary Semantic Analysis. IEEE Transactions on Multimedia 14(3):883–895CrossRefGoogle Scholar
  25. 25.
    Stocker AA (2002) An improved 2D optical flow sensor for motion segmention. Circuits and Systems. ISCSA 2002. IEEE International Symposium on Volume 2, 26-29. Page(s):332-335Google Scholar
  26. 26.
    Su X, Sun Z-s, Zhao S-m (2006) Fuzzy Control Method for Ball and Plate System. Computer Simulation 23(9):165–167Google Scholar
  27. 27.
    Wang G, Gai Q, Yu H, Wen X, Ren T (2014) Video target detection algorithms based on background subtraction. Journal of Engineering of Heilongjiang University 5(4):64–68Google Scholar
  28. 28.
    Wang Y, Sun M, Wang Z, Liu Z, Chen Z (2014) A novel disturbance-observer based friction compensation scheme for ball and plate system. ISA Trans 53:671–678CrossRefGoogle Scholar
  29. 29.
    Wang L, Xu L et al (2017) Straw Coverage Detection Method Based on Sauvola and Otsu Segmentation Algorithm. Agric Eng 17(4):29–35Google Scholar
  30. 30.
    Yuangang L, Qingsheng G, Yageng S, Lin Q, Zheng C (2015) An Algorithm for Skeleton Extraction Between Map Objects. Geomatics and Information Science of Wuhan University 40(2):264–268Google Scholar
  31. 31.
    Zhang TY, Suen CY (1984) A fast parallel algorithm for thinning digital patterns. Commun ACM 27(3):236–239CrossRefGoogle Scholar
  32. 32.
    Zhao Y-H, Shao H-X (2011) Research of ball and plate control system based on vision. Industry Control and Applications 30(10):12–15Google Scholar
  33. 33.
    Zhu S (2011) Edge detection based on mathematical morphology and image fusion. Proceedings of 2011 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference, CSQRWC 2011, v 2, p 1290-1293Google Scholar
  34. 34.
    Zhu S, Giacobazzi R (2008) Hiding information in completeness holes: new perspectives in code obfuscation and watermarking. Software Engineering and Formal Methods 2008. SEFM '08. Sixth IEEE International Conference on, pp. 7-18Google Scholar
  35. 35.
    Zou J-c, Zheng W-q, Yang Z-h (2017) A novel enhancement method for low illumination images based on microarray camera. Applied Mathematics-A Journal of Chinese Universities 32:313 ISSN 1005-1031MathSciNetCrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Electrical and Electronic Experiment Center of Southeast UniversityNanjingChina
  2. 2.School of Electrical EngineeringSoutheast UniversityNanjingChina

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