Granular stochastic modeling of robot micrometric precision

The paper aims at modeling and quantifying robot precision when it is possible to obtain information from external sensors in the operating area. The author proves that in this situation, neither the pose repeatability, nor the pose accuracy are adequate to calculate the maximum position error. A new paradigm is then proposed: granulous space modeling which combines spatial resolution and actuators' repeatability. This stochastic modeling is first detailed in unidimensional space then in the case of bidimensional space. The methodology to compute the maximal position error is given and compared with other approaches.