A Two-Step Calibration Method for Industrial Robots to Improve Positioning Accuracy Under External Payloads

Accurate positioning of robot end effectors is essential for many industrial applications. Conventional robot calibration methods primarily identify deviations in kinematic parameters. At the same time, complex non-kinematic errors, such as those caused by structural deformation and varying external payloads, are often neglected, resulting in reduced positioning accuracy. To address this issue, this paper proposes a two-step calibration method to improve robot positioning accuracy under external payloads. The method is developed based on the Modified Denavit–Hartenberg (MDH) model and incorporates a data-driven error estimation strategy. In the first step, a convolutional neural network (CNN) predicts the positioning error of the robot end effector under no-load conditions. In the second step, a dual-branch convolutional neural network with an attention mechanism (DB-CNN-AM) estimates the additional positioning error induced by varying payloads. The predicted errors are combined and compensated within the MDH-based model to obtain corrected target positions. Experiments on a TB6-R10 industrial robot demonstrate the effectiveness of the proposed method. When the external payload is less than 7 kg, the maximum positioning error is reduced from 4.197 mm to 0.240 mm, and the average error decreases from 1.792 mm to 0.096 mm, corresponding to a reduction of 94.64%.