Precision measurement and compensation of kinematic errors for industrial robots using artifact and machine learning

Yu, Yi

doi:10.1007/s40436-022-00400-6

Cited by 8 publications

(2 citation statements)

References 28 publications

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“…Multiple effects have to be considered simultaneously to improve the positioning accuracy to meet industrial standards. Since this renders the modeling of industrial robots more complex, some efforts have been made to use parametric models such as neural networks [10], [11], [12], [13] or combinations of geometric and parametric approaches [9]. While these models succeed in capturing highly complex behavior, they do not generalize and thus require a large amount of measurements to cover the entire workspace and different operation modes.…”

Section: Introductionmentioning

confidence: 99%

Compensation of Geometric, Backlash, and Thermal Drift Errors Using a Universal Industrial Robot Model

Sigron,

Aschwanden,

Bambach

2024

IEEE Trans. Automat. Sci. Eng.

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In order to facilitate the use of articulated robots for complex industrial applications, methods to improve the absolute positioning accuracy are needed. This work introduces a robot-independent calibration procedure that allows to compensate for geometric and backlash errors as well as for thermal drift. To this end, a unifying robot model is introduced based on the product of exponentials formula that complies with the requirements of the calibration process. The model is able to represent any kind of serial kinematic chain, which guarantees universal applicability. The full parameter identification is performed simultaneously based on a single data set containing all modeled effects. The individual modeling approaches have been verified experimentally using two different industrial robots. The positioning accuracy could be improved to industrial standards within the selected workspace. More precisely, the average position error was reduced from 3.39mm to 0.13mm for a KUKA KR-16 robot and from 0.36mm to 0.12mm for a KUKA KR-30 within all operating conditions. The generalizing property of the thermal expansion model was assessed by compensating the thermal drift for an unseen and fundamentally different thermal excitation of the robot structure, limiting the thermally induced positioning error to 0.1mm.Note to Practitioners-This paper was motivated by the fact that modern industrial robots are known to be able to execute the same movement repeatedly with high precision but are unable to visit an arbitrary sequence of commanded positions with high accuracy. In practice, this means that each pose of a given process must be taught to the robot by moving it to the target position and storing the robot's posture. To overcome this limitation, this work introduces a robot model that is able to express the geometric properties of the robot, as well as the joint backlash and the thermal expansion of the robot links. The model parameters must be evaluated based on a measurement sequence of 300-400 robot poses during the heating up of the robot structure. Therefore, depending on the size of the robot, the calibration procedure takes one to four hours. To use the thermal expansion correction, realtime measurements based on four low-cost temperature sensors are used. Since the modeled robot characteristics only change due to long-term wear, there is no need to repeat the calibration procedure frequently. The introduced calibration method allows to move the robot to arbitrary positions or along arbitrary trajectories within the employed workspace, with a positioning error of less than 0.2mm. This facilitates non-repetitive pick and place, assembly, or additive manufacturing tasks.

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Section: Introductionmentioning

confidence: 99%

Compensation of Geometric, Backlash, and Thermal Drift Errors Using a Universal Industrial Robot Model

Sigron,

Aschwanden,

Bambach

2024

IEEE Trans. Automat. Sci. Eng.

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“…In addition to positioning errors caused by geometric factors, nongeometric factors, such as gear gap, joint deformation, and temperature change, also affect the end positioning accuracy of robots [18]. The error mechanisms affecting robot positioning accuracy are complex and interconnected [19].…”

Section: Introductionmentioning

confidence: 99%

An Off-Line Error Compensation Method for Absolute Positioning Accuracy of Industrial Robots Based on Differential Evolution and Deep Belief Networks

Tao,

Liu,

Chen

et al. 2023

Electronics

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Industrial robots have been increasingly used in the field of intelligent manufacturing. The low absolute positioning accuracy of industrial robots is one of the difficulties in their application. In this paper, an accuracy compensation algorithm for the absolute positioning of industrial robots is proposed based on deep belief networks using an off-line compensation method. A differential evolution algorithm is presented to optimize the networks. Combined with the evidence theory, a position error mapping model is proposed to realize the absolute positioning accuracy compensation of industrial robots. Experiments were conducted using a laser tracker AT901-B on an industrial robot KR6_R700 sixx_CR. The absolute position error of the end of the robot was reduced from 0.469 mm to 0.084 mm, improving the accuracy by 82.14% after the compensation. Experimental results demonstrated that the proposed compensation algorithm could improve the absolute positioning accuracy of industrial robots, as well as its potential uses for precise operational tasks.

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A novel approach for robot calibration based on measurement sub-regions with comparative validation

Toquica,

S.T. Motta

2024

Int J Adv Manuf Technol

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Precision measurement and compensation of kinematic errors for industrial robots using artifact and machine learning

Cited by 8 publications

References 28 publications

Compensation of Geometric, Backlash, and Thermal Drift Errors Using a Universal Industrial Robot Model

Compensation of Geometric, Backlash, and Thermal Drift Errors Using a Universal Industrial Robot Model

An Off-Line Error Compensation Method for Absolute Positioning Accuracy of Industrial Robots Based on Differential Evolution and Deep Belief Networks

A novel approach for robot calibration based on measurement sub-regions with comparative validation

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