Optimal Path Evolution in a Dynamic Distributed MEMS-Based Conveyor

  • Haithem Skima
  • Eugen Dedu
  • Julien Bourgeois
  • Christophe Varnier
  • Kamal Medjaher
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 470)


We consider a surface designed to convey fragile and tiny micro-objects. It is composed of an array of decentralized blocks that contain MEMS valves. We are interested in the dynamics of the optimal path between two blocks in the surface. The criteria used for optimal paths are related to the degradation of the MEMS, namely its remaining useful life and its transfer time. We study and analyze the evolution of the optimal path in dynamic conditions in order to maintain as long as possible a good performance of the conveying surface. Simulations show that during usage the number of optimal paths increases, and that position of sources greatly influences surface lifetime.


Optimal Path Transfer Time Degradation Model Principal Criterion Network Case 
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.



This work has been supported by the Région Franche-Comté and the ACTION Labex project (contract ANR-11-LABX-0001-01).


  1. 1.
    Baraldi, P., Mangili, F., Zio, E.: A kalman filter-based ensemble approach with application to turbine creep prognostics. IEEE Trans. Reliab. 61(4), 966–977 (2012)CrossRefGoogle Scholar
  2. 2.
    Blough, D.M., Santi, P.: Investigating upper bounds on network lifetime extension for cell-based energy conservation techniques in stationary ad hoc networks. In: International Conference on Mobile Computing and Networking, pp. 183–192, Atlanta, GA, USA (Sep 2002)Google Scholar
  3. 3.
    Dahroug, B., Laurent, G.J., Guelpa, V., Fort-Piat, L., et al.: Design, modeling and control of a modular contactless wafer handling system. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 976–981. IEEE (2015)Google Scholar
  4. 4.
    Floréen, P., Kaski, P., Kohonen, J., Orponen, P.: Lifetime maximization for multicasting in energy-constrained wireless networks. IEEE J. Sel. Areas Commun. 23(1), 117–126 (2005)CrossRefzbMATHGoogle Scholar
  5. 5.
    Fukuta, Y., Chapuis, Y.A., Mita, Y., Fujita, H.: Design, fabrication, and control of mems-based actuator arrays for air-flow distributed micromanipulation. J. Microelectromech. Syst. 15(4), 912–926 (2006)CrossRefGoogle Scholar
  6. 6.
    Huang, H., Gao, S.: Optimal paths in dynamic networks with dependent random link travel times. Transp. Res. Part B 46(5), 579–598 (2012)CrossRefGoogle Scholar
  7. 7.
    Javed, K.: A robust and reliable data-driven prognostics approach based on extreme learning machine and fuzzy clustering. Ph.D. thesis, University of Franche-Comté, Besançon, France (2014)Google Scholar
  8. 8.
    Jay, L., Fangji, W., Wenyu, Z., Masoud, G., Linxia, L., David, S.: Prognostics and health management design for rotary machinery systems reviews, methodology and applications. Mech. Syst. Signal Process. 42(1), 314–334 (2014)Google Scholar
  9. 9.
    Kang, I., Poovendran, R.: On lifetime extension and route stabilization of energy-efficient broadcast routing over MANET. In: International Network Conference, pp. 81–88, London, UK (Jul 2002)Google Scholar
  10. 10.
    Kirby, B.T., Ashley-Rollman, M., Goldstein, S.C.: Blinky blocks: a physical ensemble programming platform. In: CHI’11 Extended Abstracts on Human Factors in Computing Systems, pp. 1111–1116. ACM, Vancouver, Canada (2011)Google Scholar
  11. 11.
    Konishi, S., Fujita, H.: A conveyance system using air flow based on the concept of distributed micro motion systems. J. Microelectromech. Syst. 3(2), 54–58 (1994)CrossRefGoogle Scholar
  12. 12.
    Kurokawa, H., Tomita, K., Kamimura, A., Kokaji, S., Hasuo, T., Murata, S.: Distributed self-reconfiguration of M-TRAN III modular robotic system. Int. J. Robot. Res. 27(3–4), 373–386 (2008)CrossRefGoogle Scholar
  13. 13.
    Matmat, M., Koukos, K., Coccetti, F., Idda, T., Marty, A., Escriba, C., Fourniols, J.Y., Estève, D.: Life expectancy and characterization of capacitive RF MEMS switches. Microelectron. Reliab. 50(9), 1692–1696 (2010)CrossRefGoogle Scholar
  14. 14.
    Medjaher, K., Zerhouni, N.: Hybrid prognostic method applied to mechatronic systems. Int. J. Adv. Manuf. Technol. 69(1–4), 823–834 (2013)CrossRefGoogle Scholar
  15. 15.
    Medjaher, K., Skima, H., Zerhouni, N.: Condition assessment and fault prognostics of microelectromechanical systems. Microelectron. Reliab. 54(1), 143–151 (2014)CrossRefGoogle Scholar
  16. 16.
    Salemi, B., Moll, M., Shen, W.M.: SUPERBOT: a deployable, multi-functional, and modular self-reconfigurable robotic system. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3636–3641, Beijing, China (Oct 2006)Google Scholar
  17. 17.
    Shea, H.R.: Reliability of MEMS for space applications. In: MOEMS-MEMS 2006 Micro and Nanofabrication, pp. 61110A–61110A. International Society for Optics and Photonics (2006)Google Scholar
  18. 18.
    Skima, H., Medjaher, K., Varnier, C., Dedu, E., Bourgeois, J.: Hybrid prognostic approach for Micro-Electro-Mechanical Systems. In: IEEE Aerospace Conference, pp. 1–8. 36, Big Sky, Montana, USA (Mar 2015)Google Scholar
  19. 19.
    Tanner, D.M.: MEMS reliability: where are we now? Microelectron. Reliab. 49(9), 937–940 (2009)CrossRefGoogle Scholar
  20. 20.
    Yin, S., Zhu, X.: Intelligent particle filter and its application on fault detection of nonlinear system. IEEE Trans. Industr. Electron. 62(6), 3852–3861 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Haithem Skima
    • 1
  • Eugen Dedu
    • 1
  • Julien Bourgeois
    • 1
  • Christophe Varnier
    • 1
  • Kamal Medjaher
    • 1
  1. 1.Institut FEMTO-ST, UMR CNRS 6174 - UFC/ENSMMUniversité Bourgogne Franche-Comté (UBFC)BesançonFrance

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