Published: 11 January 2023| Version 1 | DOI: 10.17632/srxktn4752.1
Teng Zhang, Fangyu Peng, Xiaowei Tang, Rong Yan, Chi Zhang, Runpeng Deng


Numerous factors cause robot positional errors such as robot manufacturing, assembly, drive, clearance, friction, etc. They account for about 60% to 70% of the overall robot errors. It is an unavoidable research priority in industrial robotics research. In order to allow more robotics researchers to quickly validate the methods conceived, this dataset is hereby made public. This dataset consists of five parts, the first of which is the spatial discrete points for the kinematic calibration of the robot, and the remaining four are defined as POINTS, LINE, CURVE, and SURFACE, respectively, for a number of points in the working space of the Staubli TX2-90L robot and for the spatial motion set in the working range of ISO 9283-1998. and simulated CAM trajectories. The dataset can be used for 1) the calibration of robot kinematic parameters, 2) the analysis of the correlation between robot pose errors and robot joint angles, 3) the construction of a robust and generalized intelligent model for the sensing of robot end pose errors, and 4) the investigation of new ideas and methods for the compensation of robot pose errors and their practical numerical validation. On the one hand, this dataset can fill the gap in the field of robot pose error research in terms of publicly available datasets, and on the other hand, it provides a large number of high-quality measurement results through high-precision measurement means, which provides an inherently reliable data support for the development of relevant intelligent algorithms.



Huazhong University of Science and Technology


Robotics, Error Analysis, Manufacturing Robotics


National Natural Science Foundation of China


National Natural Science Foundation of China


National Natural Science Foundation of China