Fuzzy inference mechanism for recognition of contact states in intelligent robotic assembly
Само за регистроване кориснике
2014
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
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.
Кључне речи:
Support vector machines / Part mating / Fuzzy inference mechanism / Contact statesИзвор:
Journal of Intelligent Manufacturing, 2014, 25, 3, 571-587Издавач:
- Springer, Dordrecht
Финансирање / пројекти:
DOI: 10.1007/s10845-012-0706-x
ISSN: 0956-5515
WoS: 000336223900013
Scopus: 2-s2.0-84901417574
Колекције
Институција/група
Mašinski fakultetTY - JOUR AU - Jakovljević, Živana AU - Petrović, Petar AU - Miković, Vladimir Dj. AU - Pajić, Miroslav PY - 2014 UR - https://machinery.mas.bg.ac.rs/handle/123456789/1886 AB - 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. PB - Springer, Dordrecht T2 - Journal of Intelligent Manufacturing T1 - Fuzzy inference mechanism for recognition of contact states in intelligent robotic assembly EP - 587 IS - 3 SP - 571 VL - 25 DO - 10.1007/s10845-012-0706-x ER -
@article{ author = "Jakovljević, Živana and Petrović, Petar and Miković, Vladimir Dj. and Pajić, Miroslav", year = "2014", 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.", publisher = "Springer, Dordrecht", journal = "Journal of Intelligent Manufacturing", title = "Fuzzy inference mechanism for recognition of contact states in intelligent robotic assembly", pages = "587-571", number = "3", volume = "25", doi = "10.1007/s10845-012-0706-x" }
Jakovljević, Ž., Petrović, P., Miković, V. Dj.,& Pajić, M.. (2014). Fuzzy inference mechanism for recognition of contact states in intelligent robotic assembly. in Journal of Intelligent Manufacturing Springer, Dordrecht., 25(3), 571-587. https://doi.org/10.1007/s10845-012-0706-x
Jakovljević Ž, Petrović P, Miković VD, Pajić M. Fuzzy inference mechanism for recognition of contact states in intelligent robotic assembly. in Journal of Intelligent Manufacturing. 2014;25(3):571-587. doi:10.1007/s10845-012-0706-x .
Jakovljević, Živana, Petrović, Petar, Miković, Vladimir Dj., Pajić, Miroslav, "Fuzzy inference mechanism for recognition of contact states in intelligent robotic assembly" in Journal of Intelligent Manufacturing, 25, no. 3 (2014):571-587, https://doi.org/10.1007/s10845-012-0706-x . .