Extraction of robot primitive control rules from natural language instructions
Extraction of robot primitive control rules from natural language instructions
复制标题
从自然语言指令中提取机器人原语控制规则
DOI：
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发表时间：
2006
影响因子：
4.3
通讯作者：
Zu
中科院分区：
文献类型：
--
作者：
Guang;Ping Jiang;Zu
研究了一种基于支持向量机规则的自然语言训练运动控制器的构造方法。它是一个两阶段的过程，包括从自然语言指令的运动控制信息的收集，和运动信息的压缩与支持向量机（SVM）理论的帮助。自组织模糊神经网络用于控制规则的收集，从其中提取支持向量规则，形成最终的控制器，以实现任何给定的控制精度。这样，减少了控制规则的数量，整理了控制器的结构，使得利用自然语言训练构造的控制器更适合于实际应用，为机器人的高级行为控制提供了基本的规则库。对轮式机器人进行了仿真和实验，验证了该方法的有效性。
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.