Error Reduction in Spatial Robots Based on the Statistical Uncertainty Analysis

Kinematic accuracy of the robot end-effector is decreased by many uncertainties. In order to design and manufacture robots with high accuracy, it is essential to know the effects of these uncertainties on the motion of robots. Uncertainty analysis is a useful method which can estimate deviations from desired path in robots caused by uncertainties. This paper presents an applied formulation based on Direct Linearization Method (DLM), for 3D statistical uncertainty analysis of open- loop mechanisms and robots. The maximum normal and parallel components of the position error on the end-effector path are introduced. In this paper, uncertainty effects of both linear and angular variations in performance of spatial open-loop mechanisms and robots are considered. Based on the relations for the percent contributions of manufacturing variables, for the position error reduction, the tolerances that have the most significant effects on the commutated uncertainty zone of the end-effector position can be modified. The proposed method is illustrated using a spatial manipulator with three-revolute joints and verified with a Monte Carlo simulation method. Finally, normal and parallel distances to end-effector path are determined as error bands for all over range of motion. The results of applying this method demonstrate that estimating the position error in mechanisms and robots can be done efficiently and precisely.