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.
翻译:目前的机械性物件识别取决于从运动性相互作用信号(如力、振动或位置)中得出的统计措施。 从这些信号中可以识别的机械性能是内在的物体特性,可以产生更强的物体表示。因此,本文件提出使用具有多重代表性的机械特性的物体识别框架:恢复原状系数、僵硬度、粘度和摩擦系数。这些机械性能是实时使用Kalman双倍过滤器确定的,然后用于对物体进行分类。提议的框架通过机器人通过机能勘探确定20个物体进行了测试。结果显示了技术的效能和效率,所有四种机械性能都需要得到最佳确认,从而产生98.18 $\ pm$ 0.424%的速率。与高斯混合混合物模型的组合进一步表明,与使用互动信号的统计参数相比,使用这些机械性能可以产生更高的认识。