Simultaneously Calibration of Multi Hand–Eye Robot System Based on Graph

Abstract

Precise calibration is the basis for the vision-guided robot system to achieve high-precision operations. Systems with multi-eyes (cameras) and multi-hands (robots) are particularly sensitive to calibration errors. Most existing methods focus on the calibration of a single unit of the whole system, such as poses between hand and eye, or between two hands. These methods can be used to determine the pose between each unit, but the serialized incremental calibration strategy cannot avoid the error accumulation problem in a large-scale system. Instead of focusing on a single unit, this paper models the multi-eye and multi-hand system calibration problem as a graph and proposes a method based on the minimum spanning tree and graph optimization. This method can automatically plan the serialized optimal calibration strategy in accordance with the system settings to get coarse calibration results initially. Then, with these initial values, the closed-loop constraints are introduced to carry out global optimization with different sensor's accuracy considered. As a general calibration method, it can be applied to different multi-robot systems. Simulation experiments demonstrate the performance of the proposed algorithm under different noises and various hand-eye configurations. In addition, experiments on real robot systems are presented to further verify the proposed method.