论文标题
基于机械功能的对象识别
Mechanical features based object recognition
论文作者
论文摘要
当前的机器人触觉对象识别依赖于从运动相互作用信号（例如力，振动或位置）得出的统计措施。可以从这些信号中识别的机械性能是可能产生更强大对象表示的内在对象属性。因此，本文提出了使用多种代表性机械性能的对象识别框架：恢复，刚度，粘度和摩擦系数的系数。这些机械性能是使用双Kalman过滤器实时识别的，然后用于对象进行分类。提出的框架通过机器人通过触觉勘探识别20个对象进行了测试。结果证明了该技术的有效性和效率，并且最佳识别率为98.18 $ \ pm $ 0.424％需要所有四个机械性能。与使用这些机械性能相比，与使用相互作用信号的统计参数相比，使用高斯混合模型聚类进一步表明，使用这些机械性能会导致良好的识别。
Current robotic haptic object recognition relies on statistical measures derived from movement dependent interaction signals such as force, vibration or position. Mechanical properties that can be identified from these signals are intrinsic object properties that may yield a more robust object representation. Therefore, this paper proposes an object recognition framework using multiple representative mechanical properties: the coefficient of restitution, stiffness, viscosity and friction coefficient. These mechanical properties are identified in real-time using a dual Kalman filter, then used to classify objects. The proposed framework was tested with a robot identifying 20 objects through haptic exploration. The results demonstrate the technique's effectiveness and efficiency, and that all four mechanical properties are required for best recognition yielding a rate of 98.18 $\pm$ 0.424 %. Clustering with Gaussian mixture models further shows that using these mechanical properties results in superior recognition as compared to using statistical parameters of the interaction signals.
