Abstract
Vision algorithms for robot control are usually context dependent. A state based vision controller can provide both the computational and temporal context for the algorithm. A hierarchical layering enables one or more sub-objectives to be selected. To mimic human behaviour, it is argued that using fuzzy logic is better able to manage the subjective data obtained from images. Fuzzy reasoning is used to control both the transitions between states, and also to directly control the behaviour of the robot. These principles are illustrated with an autonomous guide robot. Preliminary results indicate that this approach enables complex control and vision systems to be readily constructed in a manner that is both modular and extensible.
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Bailey, D., Wong, Y.W., Tang, L. (2008). Hierarchical Fuzzy State Controller for Robot Vision. In: Sommer, G., Klette, R. (eds) Robot Vision. RobVis 2008. Lecture Notes in Computer Science, vol 4931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78157-8_30
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DOI: https://doi.org/10.1007/978-3-540-78157-8_30
Publisher Name: Springer, Berlin, Heidelberg
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