Abstract
In this paper, fuzzy models obtained on-line and off-line for uncalibrated visual servoing, are proposed and validated for an unicycle mobile robot. This approach will recursively update the inverse fuzzy model based only on measurements, at a given time instant. The uncalibrated approach does not require calibrated kinematic and camera models, as needed in classical visual servoing to obtain the Jacobian. Experimental results obtained in an unicycle mobile robot performing eye-in-hand visual servoing are used to demonstrate the validity of the proposed approach, when compared to the previous developed off-line learning.
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Acknowledgments
This work was partly supported by the Strategic Project, PEst-OE/EME/LA0022/2011, through FCT (under IDMEC-IST, Research Group: IDMEC/LAETA/CSI), FCT project PTDC/EME-CRO/099333/2008 and FP7 project ECHORD.
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© 2013 Springer Science+Business Media Dordrecht
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Gonçalves, P.J.S., Lopes, P.J.F., Torres, P.M.B., Sequeira, J.M.R. (2013). Evolving Fuzzy Uncalibrated Visual Servoing for Mobile Robots. In: Madureira, A., Reis, C., Marques, V. (eds) Computational Intelligence and Decision Making. Intelligent Systems, Control and Automation: Science and Engineering, vol 61. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4722-7_6
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DOI: https://doi.org/10.1007/978-94-007-4722-7_6
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