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Condition Monitoring for Image-Based Visual Servoing Using Kalman Filter

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Advances in Visual Computing (ISVC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9475))

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Abstract

In image-based visual servoing (IBVS), the control law is based on the error between the current and desired features on the image plane. The visual servoing system is working well only when all the designed features are correctly extracted. To monitor the quality of feature extraction, in this paper, a condition monitoring scheme is developed. First, the failure scenarios of the visual servoing system caused by incorrect feature extraction are reviewed. Second, we propose a residual generator, which can be used to detect if a failure occurs, based on the Kalman filter. Finally, simulation results are given to verify the effectiveness of the proposed method.

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References

  1. Wilson, W.J., Hulls, C.C.W., Bell, G.S.: Relative end-effector control using certesian position based visual servoing. IEEE Trans. Robot. Autom. 15, 684–696 (1996)

    Article  Google Scholar 

  2. Keshmiri, M., Xie, W.F., Mohebbi, A.: Augmented image-based visual servoing of a manipulator using acceleration command. IEEE Trans. Ind. Electron. 61, 5444–5452 (2014)

    Article  Google Scholar 

  3. Malis, E., Chaumette, F., Boudet, S.: 2(1/2)d visual servoing. IEEE Trans. Robot. Autom. 15, 684–696 (1996)

    Google Scholar 

  4. Malis, E., Mezouar, Y., Rives, P.: Robustness of image-based visual servoing with a calibrated camera in the presence of uncertainties in the three-dimensional structure. IEEE Trans. Robot. 26, 112–120 (2010)

    Article  Google Scholar 

  5. Chaumette, F., Hutchinson, S.: Visual servoing control, part I: basic approaches. IEEE Robot. Autom. Mag. 13, 82–90 (2006)

    Article  Google Scholar 

  6. Sharifi, F.J., Deng, L., Wilson, W.J.: Comparison of basic visual servoing methods. IEEE Trans. Mechatron. 16, 967–983 (2011)

    Article  Google Scholar 

  7. Corke, P.I., Hutchinson, S.A.: A new portioned approach to image-based visual servoing control. IEEE Trans. Robot. Autom. 17, 507–515 (2001)

    Article  Google Scholar 

  8. Aracil, N.G., Malis, E., Santonja, R.A., Vidal, C.P.: Continuous visual servoing despite the changes of visibility in image features. IEEE Trans. Robot. 21, 1214–1220 (2005)

    Article  Google Scholar 

  9. Chesi, G., Hashimoto, K., Prattichizzo, D., Vicino, A.: Keeping features in the field of view in Eye-In-Hand visual servoing: A switch approach. IEEE Trans. Robot. 20, 908–913 (2004)

    Article  Google Scholar 

  10. Kazemi, M., Gupta, K.K., Mehrandezh, M.: Randomized kinodynamic planning for robust visual servoing. IEEE Trans. Robot. 29, 1197–1211 (2013)

    Article  Google Scholar 

  11. Folio, D., Cadenat, V.: A sensor based controller able to treat total image loss and to guarantee non-collision during a vision-based navigation task. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3052–3057 (2008)

    Google Scholar 

  12. Lippiello, V., Siciliano, B., Villani, L.: Position-based visual servoing in industrial multirobot cells using a hybrid camera configuration. IEEE Trans. Robot. 23, 73–86 (2007)

    Article  Google Scholar 

  13. Huang, S., Tan, K.K., Lee, T.H.: Fault diagnosis and Fault tolerant control in linear drives using the kalman filter. IEEE Trans. Ind. Electron. 59, 4285–4292 (2012)

    Article  Google Scholar 

  14. Auger, F., Hilairet, M., Guerrero, J.M., Monmasson, E., Kowalska, T.O., Katsura, S.: Industrial applications of the kalman filter: a review. IEEE Trans. Ind. Electron. 60, 5458–5471 (2013)

    Article  Google Scholar 

  15. Solanes, J.E., Armesto, L., Tornero, J., Girbes, V.: Improving image-based visual servoing with reference features filtering. In: IEEE International Conference on Robotics and Automation, pp. 3083–3088 (2013)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Advanced Robotic Centre, Faculty of Engineering, National University of Singapore, Singapore, Singapore

    Mien Van, Denglu Wu, Shuzi Sam Ge & Hongliang Ren

Authors
  1. Mien Van
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  2. Denglu Wu
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  3. Shuzi Sam Ge
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  4. Hongliang Ren
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Corresponding author

Correspondence to Hongliang Ren .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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© 2015 Springer International Publishing Switzerland

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Van, M., Wu, D., Ge, S.S., Ren, H. (2015). Condition Monitoring for Image-Based Visual Servoing Using Kalman Filter. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_79

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  • DOI: https://doi.org/10.1007/978-3-319-27863-6_79

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27862-9

  • Online ISBN: 978-3-319-27863-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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