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13. Vision-only Motion Controller for Omni-directional Mobile Robot Navigation 43 Fig. 12. (a) Positions where the image of instructor data is captured. (b)(c) Robot moves along each X, Y Zr, Zl and R axis of the place midpoint to evaluate the domain around the place Fig. 13. 2 Localization assessment A manual localization experiment was performed in order to see the ability of the robot to recognize its own position and thus localize the selected nodes. The experiment took place at the 3rd floor corridor environment of the Mechanical Engineering Department building in our university (Fig.
Feng, L. (1995). Where am I? Sensors and Techniques for Mobile Robot Positioning. , Ch. 2, pp. 28-35 and pp. 71-72, 1995. Brooks, R. & Iyengar, S. (1997). Multi-sensor Fusion - Fundamentals and Applications with Software. Printice-Hall, New Jersey 1997. Brooks, R. (1986). A Robust Layered Control System for a Mobile Robot. IEEE Journal of Robotics and Automation, Vol. RA-2, No 1, pp. 14-23, March 1986. A. (1991). Intelligence Without Representation. V, No 47, pages 139-159, 1991. ; Russel, A.
Zelek, J. (1996). SPOTT: A Real-Time, Distributed and Scalable Architecture for Autonomous Mobile Robot Control. Thesis, Centre for Intelligent Machines Departament of Eletrical Engineering McGill University, 1996. 2 Vision-only Motion Controller for Omni-directional Mobile Robot Navigation Fairul Azni Jafar1, Yuki Tateno1, Toshitaka Tabata1, Kazutaka Yokota1 and Yasunori Suzuki2 1Graduate School of Engineering, Utsunomiya University, 2Toyota Motor Corporation Japan 1. Introduction A major challenge to the widespread deployment of mobile robots is the ability to function autonomously, learning useful models of environmental features, recognizing environmental changes, and adapting the learned models in response to such changes.