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.
Similar content being viewed by others
Change history
13 January 2020
The Editor-in-Chief has retracted this article (Joviæ et al. 2019) because validity of the content of this article cannot be verified. This article showed the evidence of substantial text overlap [most notably with the articles cited (Petkovic et al. 2016; Mladenovic et al. 2016)] and authorship manipulation. All authors disagree about this retraction.
13 January 2020
The Editor-in-Chief has retracted this article (Jovi�� et al. 2019) because validity of the content of this article cannot be verified. This article showed the evidence of substantial text overlap [most notably with the articles cited (Petkovic et al. 2016; Mladenovic et al. 2016)] and authorship manipulation. All authors disagree about this retraction.
References
Bezaka, P., Bozekb, P., & Nikitin, Y. (2014). Advanced robotic grasping system using deep learning. Procedia Engineering, 96(2014), 10–20.
Cai, Y. (1996). Genetic programming for prediction of earthquake sequence type. Acta Seismologica Sinica, 9, 53. doi:10.1007/BF02650623.
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.
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.
DeVore, R., Jawerth, B., & Lucier, B. (1992). Images compression through wavelet transform coding. IEEE Transactions on Information Theory, 38(2), 719–746.
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.
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.
Engel, A. (2001). Complexity of learning in artificial neural networks. Theoretical Computer Science, 265(1–2), 285–306.
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.
Galabov, V., Stoyanova, Y., & Slavov, G. (2014). Synthesis of an adaptive gripper. Applied Mathematical Modelling, 38, 3175–3181.
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.
Hopkins, J. B., & Panas, R. M. (2013). Design of flexure-based precision transmission mechanisms using screw theory. Precision Engineering, 37(2), 299–307.
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.
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.
Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006b). Extreme learning machine: Theory and applications. Neurocomputing, 70, 489–501.
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.
Jang, G., Lee, C., Lee, H., & Choi, Y. (2013). Robotic index finger prosthesis using stackable double 4-BAR mechanisms. Mechatronics, 23, 318–325.
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.
Koza, J. R. (1994). Genetic programming as a means for programming computers by natural selection. Statistics and Computing, 4, 87. doi:10.1007/BF00175355.
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.
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.
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.
Muravyev, N. V., & Pivkina, A. N. (2016). New concept of thermokinetic analysis with artificial neural networks. Thermochimica Acta, 637(10), 69–73.
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.
Nguyen, K.-D., & Dankowicz, H. (2015). Adaptive control of underactuated robots with unmodeled dynamics. Robotics and Autonomous Systems, 64, 84–99.
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.
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.
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.
Sigmund, O. (1994). Design of material structures using topology optimization. PhD thesis, Technical University of Denmark.
Sigmund, O. (1997). On the design of compliant mechanisms using topology optimization. Journal of Structural Mechanics, 25, 495–526.
Sigmund, O. (2001). A 99 line topology optimization code written in matlab. Structural and Multidisciplinary Optimization, 21, 120–127.
Singh, R., & Balasundaram, S. (2007). Application of extreme learning machine method for time series analysis. International Journal of Intelligent Technology, 2, 256–262.
Strang, G. (1993). Wavelet transforms versus Fourier transforms. Bulletin of the American Mathematical Society, 28(2), 288–305.
Xin, X., & Liu, Y. (2013). Reduced-order stable controllers for two-link underactuated planar robots. Automatica, 49(7), 2176–2183.
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.
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.
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.
Author information
Authors and Affiliations
Corresponding author
Additional information
The Editor-in-Chief has retracted this article [1] because the validity of the content of this article cannot be verified. This article showed evidence of substantial text overlap (most notably with the articles cited [2, 3]) and authorship manipulation. All authors disagree with this retraction.
References
1. Jović, S., Arsić, N., Marić, L.M. et al. J Intell Manuf (2019) 30: 891. https://doi.org/10.1007/s10845-016-1292-0
2. Dalibor Petkovic et al. Analyzing of flexible gripper by computational intelligence approach Mechatronics (2016) Vol. 40, pp 1-16 DOI 10.1016/j.mechatronics.2016.09.001
3. Igor Mladenovic et al. Extreme learning approach with wavelet transform function for forecasting wind turbine wake effect to improve wind farm efficiency Advances in Engineering Software (2016) Vol. 96, pp 91-95 DOI 10.1016/j.advengsoft.2016.02.011
Appendix
Appendix
See Table 5.
About this article
Cite this article
Jović, S., Arsić, N., Marić, L.M. et al. RETRACTED ARTICLE: Estimation of contact forces of underactuated robotic finger using soft computing methods. J Intell Manuf 30, 891–903 (2019). https://doi.org/10.1007/s10845-016-1292-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-016-1292-0