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.

翻译：随着工业机器人的广泛应用，绝对定位精度问题日益突出。为确保机器人工作状态，研究者普遍采用标定技术来提升其精度。然而，工业机器人的工作空间在实际环境中大多受限，导致采集的样本无法覆盖实际工作空间，进而产生整体数据偏移。针对这一关键问题，本文提出一种新型工业机器人标定器，将测量构型选择方法与基于多平面约束的交替方向乘子法算法集成于其工作流程中。其核心思想包含三方面：a) 基于可观测性指标选取最优测量构型组以抑制测量噪声；b) 开发AMPC算法显著提升标定精度并抑制长尾收敛现象；c) 提出融合MCS与AMPC的工业机器人标定算法，高效优化工业机器人运动学参数。为验证性能，基于HRS-JR680工业机器人构建公开数据集HRS-P。大量实验结果表明，所提标定器在标定精度上优于多种现有先进模型。