A 2-Dimensional Branch-and-Bound Algorithm for Hand-Eye Self-Calibration of SCARA Robots

摘要

Due to the high positioning accuracy and relatively low prices, SCARA robots are widely used in industrial fields. The objective of this paper is to propose a hand-eye self-calibration algorithm for SCARA robots which could consider both accuracy and computational cost. The previous global optimal hand-eye calibration algorithms based on branch-and-bound (BnB) optimization is limited by their expensive computational cost. The speed of these algorithms depends on the volume of the search space to a large extent, which is the main concern in this paper. Instead of searching over the 3-dimensional parameter space corresponding to the rotation component of hand-eye pose, a new 2-dimensional search space is defined by separating and coupling some calibration parameters by means of the special structure of SCARA robots, which have 4 degrees of freedom (DoFs) including three translation DoFs and only one rotation DoF. The simulation and real experiments show the similar accuracy but much faster speed of the proposed algorithm compared with previous optimal algorithms based on BnB.