Abstract
In this paper we propose a fuzzy rule-based algorithm for solvingclassification problems related to tactile sensing. A tactile sensing systemis a robotic device which gives an image of contacting objects. While thefine form recognition problem has been widely discussed and severaltechniques have been proposed for its solution (Bayesian approach or Neuralalgorithms), less attention has been paid to the problem of deciding whetherthe object belongs to a particular class or set of objects that share acommon feature, also known as tactile primitive. As input data we considerthe sum of the normal stresses at the sensing sites. Three levels ofclassification, hierarchically connected, are analyzed and, for each level,different basis variables with their membership functions are proposed andcalibrated using a training procedure. The output is an answer regarding thefeatures of the object at each level and is related to the truth values ofthe fuzzy classes. The numerical experiences show that, at least for dataaffected by low noise level, the algorithm has a very high percentage ofcorrect answers.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bezdek, J. C. and Chuah, S. K.: Generalized K-nearest neighbor rules, Fuzzy Sets and Systems 18(1986), 237–256.
Caiti, A., Canepa, G., De Rossi, D., Germagnoli, F., Magenes, G., and Parisini, T.: Towards the realization of an artificial tactile system: Fine-form discrimination by a tensorial tactile sensor array and neural inversion algorithms, IEEE Trans. Systems Man Cybernet. 25(6) (1995), 933–946.
Carotenuto, L., Famularo, D., and Raiconi, G.: Identification of boundary conditions for an elastic body, in: Proc. 3rd Eur. Control Conf., Rome, Sept. 1995, pp. 115–122.
De Rossi, D., Nannini, A., and Domenici, C.: Biomimetic tactile sensor with stress-components discrimination capability, J. Molecular Electronics 3(1987), 173–181.
Domenici, C., De Rossi, D., Bacci, A., and Bennati, S.: Shear stress detection in an elastic layer by a piezoelectric polymer tactile sensor, IEEE Trans. Electrical Insulation 24(6) (1989), 1077–1081.
De Rossi, D., Caiti, A., Bianchi, R., and Canepa, G.: Fine-form tactile discrimination through inversion of data from a skin-like sensor, in: Proc. 1991 IEEE Int. Conf. Robotics and Automation, Vol. I, 1991, pp. 398–403.
Famularo, D.: Sensori tattili e riconoscimento di forma fine: simulazione del problema ad elementi di contorno, analisi ed applicazione delle tecniche di regolarizzazione, Ph.D. Thesis, Feb. 1995.
Fearing, R. S., Rise, A., and Binford, T. O.:A Tactile sensing finger tip for a dextrous hand, in: 5th SPIE Intelligent Robotics and Computer Vision, MA, Cambridge, 1986, pp. 1–10.
Mendel, J. M.: Fuzzy logic systems for engineering: A tutorial, Proc. IEEE 38(3) (1995), 345–377.
Nicholls, H. R. and Lee, M. H.: A survey of robot tactile sensing technology, Int. J. Robotics Research 8(3) (1989), Massachussets Institute of Technology.
Pal, S. K. and Mitra, S.: Multilayer perceptron, fuzzy sets and classification, IEEE Trans. Neural Networks 3(5) (1992), 683–697.
Simpson, P. K.: Fuzzy min–max neural networks–Part 1: Classification, IEEE Trans. Neural Networks 3(5) (1992).
Stansfield, S. A.: Primitives, features and exploration procedures: building a robot tactile perception system, IEEE Trans. Systems Man Cybernet. 16(6) (1986).
Uebele, V., Abe, S., and Lan, M. S.: A neural network fuzzy classifier, IEEE Trans. Systems Man Cybernet. 25(2) (1995), 353–361.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Carotenuto, L., Famularo, D., Muraca, P. et al. A Fuzzy Classifier for Tactile Sensing. Journal of Intelligent and Robotic Systems 20, 71–86 (1997). https://doi.org/10.1023/A:1007913228480
Issue Date:
DOI: https://doi.org/10.1023/A:1007913228480