Fuzzy inference mechanism for recognition of contact states in intelligent robotic assembly
- 390 Downloads
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.
KeywordsPart mating Contact states Support vector machines Fuzzy inference mechanism
Fuzzy inference mechanism
Support vector machines
Discrete wavelet transform
Fuzzy rule based systems
Center of area
Unable to display preview. Download preview PDF.
- Ayağ, Z., Samanlioglu, F., & Büyüközkan, G. (2012). A fuzzy QFD approach to determine supply chain management strategies in the diary industry. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-012-0639-4.
- Bruyninckx, H., Dutre, S., & De Schutter, J. (1995). Peg-on-hole: A model based solution to peg and hole alignment. In Proceedings of the IEEE international conference on robotics and automation (pp. 1919–1924). Nagoya: IEEE.Google Scholar
- Chen, S., Wang, J., & Wang, D. (2008). Extraction of fuzzy rules by using support vector machines. In Proceedings of the 2008 fifth international conference on fuzzy systems and knowledge discovery (pp. 438–442). Washington: IEEE Computer Society.Google Scholar
- Daubechies, I. (1992). Ten lectures on wavelets, CBMS-NSF regional conference series in applied mathematics, Vol. 61. Philadelphia: Society for Industrial and Applied Mathematics.Google Scholar
- De Schutter J., Bruyninckx H., Dutré S., De Geeter J., Katupitiya J., Demeery S., Lefebvre T. (1999) Estimating first-order geometric parameters and monitoring contact transitions during forcecontrolled compliant motions. The International Journal of Robotics Research 18(12): 1161–1184CrossRefGoogle Scholar
- Hirukawa H., Matsui T., Takase K. (1994) Automatic determination of possible velocity and applicable force of frictionless objects in contact from a geometric model. IEEE Transactions on Robotics and Automation 10: 3–309322Google Scholar
- Huang, X., Shi, F., & Chen, S. (2007). A new support vector machine-based fuzzy system with high comprehensibility. In T. J. Tarn, S. B. Chen, & C. Zhou (Eds.), Robotic welding, intelligence and automation (pp. 421–427). Berlin: Springer.Google Scholar
- Jakovljevic, Z., & Petrovic, P. B. (2010). Recognition of contact states in robotized assembly using qualitative wavelet based features and support vector machines. In S. Hinduja, & L. Li (Eds.), Proceedings of the 36th international MATADOR conference (pp. 305–308). London: Springer.Google Scholar
- Jakovljevic, Z., Petrovic, P. B., & Hodolic, J. (2011). Contact states recognition in robotic part mating based on support vector machines. The International Journal of Advanced Manufacturing Technology. doi: 10.1007/s00170-011-3501-5.
- Jiménez, P., (2011). Survey on assembly sequencing: A combinatorial and geometrical perspective. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-011-0578-5.
- Kovac, P., Rodic, D., Pucovsky V., Savkovic, B., & Gostimirovic, M. (2012). Application of fuzzy logic and regression analysis for modeling surface roughness in face milliing. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-012-0623-z.
- Li, W., Yang, Y., & Yang, Z. (2006). T-S Fuzzy Modeling Based on Support Vector Learning. In D. S. Huan, K. Li, & G. W. Irwin (Eds.), Intelligent computing (pp. 1294–1299). Berlin: Springer.Google Scholar
- Nuttin, M., Rosell, J., Suárez, R., Van Brussel, H., Basañez, L., & Hao, J. (1995). Learning approaches to contact estimation in assembly tasks with robots, In M. Kaiser (Ed.), Proceedings of the 3rd European workshop on learning robots (EWLR-3) (pp. 1–11).Google Scholar
- Platt, J. C. (1999). Fast training of support vector machines using sequential minimal optimization. In B. Schoelkopf, C. J. C. Burges, & A. J. Smola (Eds.), Advances in Kernel methods: Support vector learning (pp. 185–208). Cambridge: MIT Press.Google Scholar
- Schulteis T. M., Dupon P. E., Millman P. A., Howe R. D. (1996) Automatic identification of remote environments. Proceedings of ASME Dynamic Systems and Control Division 58: 451–458Google Scholar
- Spreng, M. (1993). A probabilistic method to analyze ambiguous contact situations. In Proceedings of IEEE international conference on robotics and automation, pp. 543–548.Google Scholar
- Su, J., Qiao, H., Liu, C., & Ou, Z. (2011). A new insertion strategy for a peg in an unfixed hole of the piston rod assembly. The International Journal of Advanced Manufacturing Technology. doi: 10.1007/s00170-011-3569-y.
- Suárez, R., Basañez, L., & Rosell, J. (1994). Assembly contact force domains in the presence of uncertainty. In Proceedings of Fourth IFAC Symposium on Robot Control, pp. 653–659.Google Scholar
- Xiao, J., & Liu, L. (1998): Contact states, representation and recognizability in the presence of uncertainties. In Proceedings of international conference on intelligent robots and systems, pp. 1151–1156.Google Scholar