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Precise calibration is the basis for the vision-guided robot system toachieve high-precision operations. Systems with multiple eyes (cameras) andmultiple hands (robots) are particularly sensitive to calibration errors, suchas micro-assembly systems. Most existing methods focus on the calibration of asingle unit of the whole system, such as poses between hand and eye, or betweentwo hands. These methods can be used to determine the relative pose betweeneach unit, but the serialized incremental calibration strategy cannot avoid theproblem of error accumulation in a large-scale system. Instead of focusing on asingle unit, this paper models the multi-eye and multi-hand system calibrationproblem as a graph and proposes a method based on the minimum spanning tree andgraph optimization. This method can automatically plan the serialized optimalcalibration strategy in accordance with the system settings to get coarsecalibration results initially. Then, with these initial values, the closed-loopconstraints are introduced to carry out global optimization. Simulationexperiments demonstrate the performance of the proposed algorithm underdifferent noises and various hand-eye configurations. In addition, experimentson real robot systems are presented to further verify the proposed method.

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