  Precise calibration is the basis for the vision-guided robot system to
achieve high-precision operations. Systems with multiple eyes (cameras) and
multiple hands (robots) are particularly sensitive to calibration errors, such
as micro-assembly systems. 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 relative pose between
each unit, but the serialized incremental calibration strategy cannot avoid the
problem of error accumulation 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. 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.
