Industrial robots play a vital role in automatic production, which have been widely utilized in industrial production activities, like handling and welding. However, due to an uncalibrated robot with machining tolerance and assembly tolerance, it suffers from low absolute positioning accuracy, which cannot satisfy the requirements of high-precision manufacture. To address this hot issue, we propose a novel calibration method based on an unscented Kalman filter and variable step-size Levenberg-Marquardt algorithm. This work has three ideas: a) proposing a novel variable step-size Levenberg-Marquardt algorithm to addresses the issue of local optimum in a Levenberg-Marquardt algorithm; b) employing an unscented Kalman filter to reduce the influence of the measurement noises; and c) developing a novel calibration method incorporating an unscented Kalman filter with a variable step-size Levenberg-Marquardt algorithm. Furthermore, we conduct enough experiments on an ABB IRB 120 industrial robot. From the experimental results, the proposed method achieves much higher calibration accuracy than some state-of-the-art calibration methods. Hence, this work is an important milestone in the field of robot calibration.
翻译:工业机器人在自动生产中发挥着关键作用,在工业生产活动中广泛使用,如处理和焊接。然而,由于一个未经校准的机器人具有机敏容容度和组装容容容度,因此其绝对定位精度较低,无法满足高精度制造的要求。为了解决这一热点问题,我们提议了一种新型校准方法,该方法基于一种不精度的卡尔曼过滤器和可变的职档尺寸Levenberg-Marquardt算法。这项工作有三个想法:(a) 提出一种新的可变级级Levenberg-Marquardt算法,以解决Levenberg-Marquardt算法中当地最佳校准问题;(b) 使用一种不精度的卡尔曼过滤器,以减少测量噪音的影响;以及(c) 开发一种新型校准方法,其中包括一种不精度的卡尔曼过滤器,配有可变级档尺寸Levenberg-Marquardt算法。此外,我们对ABBRB 120个工业机器人进行了足够的实验实验。根据实验结果,拟议的方法取得了比某些州级校准方法的校准率要高得多。因此,因此,在校准方法的校准领域中,这是重要的一个重要。