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中文摘要：
为了对机器人运行状态进行有效的识别,提出一种基于支持向量机的多传感器数据两级融合方法,从分类的角度实现了运行状态识别,解决了识别正确率较低的问题.将此方法应用于两轮自平衡机器人进行运行状态识别实验,当每种状态采集的独立样本数超过20个时,正确率可以达到98%以上.实验结果表明应用该方法可以对两轮自平衡机器人的运行状态进行有效、可靠的识别,能够满足两轮自平衡机器人快速机动过程中的实时性要求.
英文摘要：
To recognize the running state of the robot efficiently, a SVM-based multisensory two-graded data fusion method is presented. The running state recognition is realized from the view of classification. The problem that the classified accuracy is low is solved. The method is applied to the two-wheeled self-balanced robot and the experiments of the running state recognition are conducted. When the individual sample number of each running state exceeds twenty the accuracy of fusion method will be above 98%. Experimental results demonstrate that the running state of the two-wheeled self-balanced robot could be recognized efficiently and reliably. The real-time requirement will be suitable in the fast and maneuverable process.
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