  With the widespread application of industrial robots, the problem of absolute
positioning accuracy becomes increasingly prominent. To ensure the working
state of the robots, researchers commonly adopt calibration techniques to
improve its accuracy. However, an industrial robot's working space is mostly
restricted in real working environments, making the collected samples fail in
covering the actual working space to result in the overall migration data. To
address this vital issue, this work proposes a novel industrial robot
calibrator that integrates a measurement configurations selection (MCS) method
and an alternation-direction-method-of-multipliers with multiple planes
constraints (AMPC) algorithm into its working process, whose ideas are
three-fold: a) selecting a group of optimal measurement configurations based on
the observability index to suppress the measurement noises, b) developing an
AMPC algorithm that evidently enhances the calibration accuracy and suppresses
the long-tail convergence, and c) proposing an industrial robot calibration
algorithm that incorporates MCS and AMPC to optimize an industrial robot's
kinematic parameters efficiently. For validating its performance, a
public-available dataset (HRS-P) is established on an HRS-JR680 industrial
robot. Extensive experimental results demonstrate that the proposed calibrator
outperforms several state-of-the-art models in calibration accuracy.
