An Efficient Industrial Robot Calibrator With Multiplaner Constraints

Calibration technology has become an essential part of the task of improving robot precision. However, existing calibration schemes suffer from accuracy loss in actual working conditions due to the ignorance of the target robot's constrained workspace. In response to this challenge, this study proposes a novel robot calibrator with three-fold ideas: 1) optimizing the measurement configuration via the measurement configurations selection (MCS) for suppressing the measurement noises, 2) developing the alternation-direction-method-of-multipliers with multiplanar constraints (AMPC) algorithm for superior calibration accuracy and mitigatory long-tail convergence, with its convergence being theoretically proved, and 3) building the MCS-AMPC-based robot calibrator for efficient kinematic parameters calibration. For validating its performance, the HRS-P dataset that contains 2400 samples collected from different working spaces of an HRS-JR680 industrial robot has been established and publicly released. Empirical studies conclusively affirm that, compared to the Levenberg–Marquardt algorithm (the most accurate among several state-of-the-art algorithms), the proposed AMPC algorithm reduces the mean error by 10.34%. After denoising the calibration data with MCS and then employing the AMPC algorithm to identify the robot's kinematic parameters, the calibration accuracy can be further improved. Moreover, building an ensemble to address the issue of various local optimal solutions can also contribute to better calibration accuracy. The empirical results highlight the superiority and scalability of the MCS-AMPC calibrator for industrial robot-related applications.