Guang-Hong Wang, Ping Jiang and Zu-Ren Feng. Extraction of Robot Primitive Control Rules from Natural Language Instructions. International Journal of Automation and Computing, vol. 3, no. 3, pp. 282-290, 2006. DOI: 10.1007/s11633-006-0282-7
Citation: Guang-Hong Wang, Ping Jiang and Zu-Ren Feng. Extraction of Robot Primitive Control Rules from Natural Language Instructions. International Journal of Automation and Computing, vol. 3, no. 3, pp. 282-290, 2006. DOI: 10.1007/s11633-006-0282-7

Extraction of Robot Primitive Control Rules from Natural Language Instructions

  • A support vector rule based method is investigated for the construction of motion controllers via natural language training. It is a two-phase process including motion control information collection from natural language instructions, and motion information condensation with the aid of support vector machine (SVM) theory. Self-organizing fuzzy neural networks are utilized for the collection of control rules, from which support vector rules are extracted to form a final controller to achieve any given control accuracy. In this way, the number of control rules is reduced, and the structure of the controller tidied, making a controller constructed using natural language training more appropriate in practice, and providing a fundamental rule base for high-level robot behavior control. Simulations and experiments on a wheeled robot are carried out to illustrate the effectiveness of the method.
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