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Journal of Intelligent Manufacturing

, Volume 25, Issue 3, pp 571–587 | Cite as

Fuzzy inference mechanism for recognition of contact states in intelligent robotic assembly

  • Zivana Jakovljevic
  • Petar B. Petrovic
  • Vladimir Dj. Mikovic
  • Miroslav Pajic
Article

Abstract

This paper presents a methodology for generating a fuzzy inference mechanism (FIM) for recognizing contact states within robotic part mating using active compliant motion. In the part mating process, significant uncertainties are inherently present. As a result it is pertinent that contact states recognition systems operating in such environment be able to make decisions on the contact state currently present in the process, based on data full of uncertainties and imprecision. In such conditions, implementation of fuzzy logic and interval inference brings significant robustness to the system. As a starting point for FIM generation, we use a quasi-static model of the mating force between objects. By applying Discrete Wavelet Transform to the signal generated using this model, we extract qualitative and representative features for classification into contact states. Thus, the obtained patterns are optimally classified using support vector machines (SVM). We exploit the equivalence of SVM and Takagi–Sugeno fuzzy rules based systems for generation of FIM for classification into contact states. In this way, crisp granulation of the feature space obtained using SVM is replaced by optimal fuzzy granulation and robustness of the recognition system is significantly increased. The information machine for contact states recognition that is designed using the given methodology simultaneously uses the advantages of creation of machine based on the process model and the advantages of application of FIM. Unlike the common methods, our approach for creating a knowledge base for the inference machine is neither heuristic, intuitive nor empirical. The proposed methodology was elaborated and experimentally tested using an example of a cylindrical peg in hole as a typical benchmark test.

Keywords

Part mating Contact states Support vector machines Fuzzy inference mechanism 

Abbreviations

FIM

Fuzzy inference mechanism

SVM

Support vector machines

NN

Neural network

MO

Moving object

EO

Environmental object

CS

Contact state

DWT

Discrete wavelet transform

FRBS

Fuzzy rule based systems

TS

Takagi–Sugeno

CC

Compliance center

COA

Center of area

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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Zivana Jakovljevic
    • 1
  • Petar B. Petrovic
    • 1
  • Vladimir Dj. Mikovic
    • 2
  • Miroslav Pajic
    • 3
  1. 1.Department for Production Engineering, Faculty of Mechanical EngineeringUniversity of BelgradeBelgradeSerbia
  2. 2.Center for ICT, Faculty of Mechanical EngineeringUniversity of BelgradeBelgradeSerbia
  3. 3.Department of Electrical and Systems EngineeringUniversity of PennsylvaniaPhiladelphiaUSA

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