Advertisement

Journal of Intelligent Manufacturing

, Volume 30, Issue 2, pp 891–903 | Cite as

RETRACTED ARTICLE: Estimation of contact forces of underactuated robotic finger using soft computing methods

  • Srđan Jović
  • Nebojša Arsić
  • Ljubomir M. Marić
  • Dalibor PetkovićEmail author
Article

Abstract

Underactuated robotic finger could be used as adaptive mechanism with simple control algorithm. In this study the main aim was to estimate the robotic finger contact forces by soft computing methods. Soft computing approach was applied in order to overcome high nonlinearity in the finger behavior. Kinetostatic analysis was performed in order to extract the input/output data samples for the soft computing methods. The main goal was to estimate the contact forces based on contact locations with the objects. Seven soft computing methods were applied: genetic programming, support vector machine, support vector machine with firefly algorithm, artificial neural network, support vector machine with wavelet transfer function), extreme learning machine and extreme learning machine with wavelet transfer function. The reliability of these computational models was analyzed based on simulation results. Extreme learning machine with wavelet transfer function shown the best accuracy for the contact forces estimation.

Keywords

Finger Soft computing Prediction Kinetostatic analysis Contact forces 

References

  1. Bezaka, P., Bozekb, P., & Nikitin, Y. (2014). Advanced robotic grasping system using deep learning. Procedia Engineering, 96(2014), 10–20.CrossRefGoogle Scholar
  2. Cai, Y. (1996). Genetic programming for prediction of earthquake sequence type. Acta Seismologica Sinica, 9, 53. doi: 10.1007/BF02650623.CrossRefGoogle Scholar
  3. Cai, J., Deng, X., Feng, J., & Xu, Y. (2014). Mobility analysis of generalized angulated scissor-like elements with the reciprocal screw theory. Mechanism and Machine Theory, 82, 256–265.CrossRefGoogle Scholar
  4. de Oliveira, F. A., Nobre, C. N., & Zárate, L. E. (2013). Applying Artificial Neural Networks to prediction of stock price and improvement of the directional prediction index–Case study of PETR4, Petrobras, Brazil. Expert Systems with Applications, 40(18), 7596–7606.CrossRefGoogle Scholar
  5. DeVore, R., Jawerth, B., & Lucier, B. (1992). Images compression through wavelet transform coding. IEEE Transactions on Information Theory, 38(2), 719–746.CrossRefGoogle Scholar
  6. Ding, S., Zhao, H., Zhang, Y., et al. (2015). Extreme learning machine: algorithm, theory and applications. Artif Intell Rev, 44, 103. doi: 10.1007/s10462-013-9405-z.CrossRefGoogle Scholar
  7. Domínguez-López, J. A., Damper, R. I., Crowder, R. M., & Harris, C. J. (2004). Adaptive neurofuzzy control of a robotic gripper with on-line machine learning. Robotics and Autonomous Systems, 48(2–3), 93–110.CrossRefGoogle Scholar
  8. Engel, A. (2001). Complexity of learning in artificial neural networks. Theoretical Computer Science, 265(1–2), 285–306.CrossRefGoogle Scholar
  9. Froio, A., Bonifetto, R., Carli, S., Quartararo, A., Savoldi, L., & Zanino, R. (2016). Design and optimization of Artificial Neural Networks for the modelling of superconducting magnets operation in tokamak fusion reactors. Journal of Computational Physics, 321(15), 476–491.CrossRefGoogle Scholar
  10. Galabov, V., Stoyanova, Y., & Slavov, G. (2014). Synthesis of an adaptive gripper. Applied Mathematical Modelling, 38, 3175–3181.CrossRefGoogle Scholar
  11. Gustafson, S., Ekárt, A., Burke, E., et al. (2004). Problem difficulty and code growth in genetic programming. Genetic Programming and Evolvable Machines, 5, 271. doi: 10.1023/B:GENP.0000030194.98244.e3.CrossRefGoogle Scholar
  12. Hopkins, J. B., & Panas, R. M. (2013). Design of flexure-based precision transmission mechanisms using screw theory. Precision Engineering, 37(2), 299–307.CrossRefGoogle Scholar
  13. Huang, G. B., Chen, L., & Siew, C. K. (2006a). Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks, 17, 879–892.CrossRefGoogle Scholar
  14. Huang, G.-B., Zhu, Q. Y., & Siew, C. K. (2004). Extreme learning machine: A new learning scheme of feed forward neural networks. International Joint Conference on Neural Networks, 2, 985–990.Google Scholar
  15. Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006b). Extreme learning machine: Theory and applications. Neurocomputing, 70, 489–501.CrossRefGoogle Scholar
  16. Ishii, C., & Futatsugi, T. (2013). Design and control of a robotic forceps manipulator with screw-drive bending mechanism and extension of its motion space. Procedia CIRP, 5, 104–109.CrossRefGoogle Scholar
  17. Jang, G., Lee, C., Lee, H., & Choi, Y. (2013). Robotic index finger prosthesis using stackable double 4-BAR mechanisms. Mechatronics, 23, 318–325.CrossRefGoogle Scholar
  18. Khadse, C. B., Chaudhari, M. A., & Borghate, V. B. (2016). Conjugate gradient back-propagation based artificial neural network for real time power quality assessment. International Journal of Electrical Power & Energy Systems, 82, 197–206.CrossRefGoogle Scholar
  19. Koza, J. R. (1994). Genetic programming as a means for programming computers by natural selection. Statistics and Computing, 4, 87. doi: 10.1007/BF00175355.CrossRefGoogle Scholar
  20. Krawiec, K. (2014). Genetic programming: Where meaning emerges from program code. Genetic Programming and Evolvable Machines, 15, 75. doi: 10.1007/s10710-013-9200-2.CrossRefGoogle Scholar
  21. Kumaraswamy, U., Shunmugam, M. S., & Sujatha, S. (2013). A unified framework for tolerance analysis of planar and spatial mechanisms using screw theory. Mechanism and Machine Theory, 69, 168–184.CrossRefGoogle Scholar
  22. Liang, N. Y., Huang, G. B., Rong, H. J., Saratchandran, P., & Sundararajan, N. (2006). A fast and accurate on-line sequential learning algorithm for feedforward networks. IEEE Transactions on Neural Networks, 17, 1411–1423.CrossRefGoogle Scholar
  23. Muravyev, N. V., & Pivkina, A. N. (2016). New concept of thermokinetic analysis with artificial neural networks. Thermochimica Acta, 637(10), 69–73.CrossRefGoogle Scholar
  24. Ngeo, J. G., Tamei, T., & Shibata, T. (2014). Continuous and simultaneous estimation of finger kinematics using inputs from an EMG-to-muscle activation model. Journal of NeuroEngineering and Rehabilitation, 11, 122. doi: 10.1186/1743-0003-11-122.CrossRefGoogle Scholar
  25. Nguyen, K.-D., & Dankowicz, H. (2015). Adaptive control of underactuated robots with unmodeled dynamics. Robotics and Autonomous Systems, 64, 84–99.CrossRefGoogle Scholar
  26. Petković, D., Pavlović, N. D., Ćojbašić, Ž., Pavlović, N. T. (2013a). Adaptive neuro fuzzy estimation of underactuated robotic gripper contact forces. Expert Systems with Applications, 40(1), 281–286.Google Scholar
  27. Petković, D., Issa, M., Pavlović, N. D., Zentner, L. (2013b). Application of the TRIZ creativity enhancement approach to design of passively compliant robotic joint. International Journal of Advanced Manufacturing Technology, 67(1), 865–875.Google Scholar
  28. Premalatha, N., & Arasu, A. V. (2016). Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms. Journal of Applied Research and Technology, 14(3), 206–214.CrossRefGoogle Scholar
  29. Sigmund, O. (1994). Design of material structures using topology optimization. PhD thesis, Technical University of Denmark.Google Scholar
  30. Sigmund, O. (1997). On the design of compliant mechanisms using topology optimization. Journal of Structural Mechanics, 25, 495–526.Google Scholar
  31. Sigmund, O. (2001). A 99 line topology optimization code written in matlab. Structural and Multidisciplinary Optimization, 21, 120–127.CrossRefGoogle Scholar
  32. Singh, R., & Balasundaram, S. (2007). Application of extreme learning machine method for time series analysis. International Journal of Intelligent Technology, 2, 256–262.Google Scholar
  33. Strang, G. (1993). Wavelet transforms versus Fourier transforms. Bulletin of the American Mathematical Society, 28(2), 288–305.CrossRefGoogle Scholar
  34. Xin, X., & Liu, Y. (2013). Reduced-order stable controllers for two-link underactuated planar robots. Automatica, 49(7), 2176–2183.CrossRefGoogle Scholar
  35. Zadeh, L. A. (1992). Fuzzy logic, neural networks and soft computing. One-page course announcement of CS 294–4. Berkley: University of California at Berkley.Google Scholar
  36. Zhang, A., Lai, X., Wu, M., & She, J. (2015). Stabilization of underactuated two-link gymnast robot by using trajectory tracking strategy. Applied Mathematics and Computation, 253(15), 193–204.CrossRefGoogle Scholar
  37. Zou, H. F., Xia, G. P., Yang, F. T., & Wang, H. Y. (2007). An investigation and comparison of artificial neural network and time series models for Chinese food grain price forecasting. Neurocomputing, 70(16–18), 2913–2923.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Srđan Jović
    • 1
  • Nebojša Arsić
    • 1
  • Ljubomir M. Marić
    • 1
  • Dalibor Petković
    • 2
    Email author
  1. 1.Faculty of Technical SciencesUniversity of PrištinaKosovska, MitrovicaSerbia
  2. 2.University of Niš, Pedagogical Faculty in VranjeVranjeSerbia

Personalised recommendations