Precise Robot Calibration Method-Based 3-D Positioning and Posture Sensor

Selami, Yassine; Tao, Wei; Lv, Na; Zhao, Hui

doi:10.1109/jsen.2022.3218292

Cited by 9 publications

(1 citation statement)

References 31 publications

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“…Chen et al analyzed the joint motion of KUKA robots according to the MDH model and proposed a kinematic calibration method based on an improved beetle swarm optimization algorithm [25]. Selami et al presented a 3DPP measurement device and kinematic parameter identification method based on the MDH model for the calibration of 6 degrees-of-freedom (DOF) industrial manipulators [26].…”

Section: Introductionmentioning

confidence: 99%

Error Modeling and Parameter Calibration Method for Industrial Robots Based on 6-DOF Position and Orientation

Lao,

Quan,

Wang

et al. 2023

Applied Sciences

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The positional accuracy and orientation accuracy of industrial robots are crucial technical indicators for determining their applicability in industrial scenarios. However, the majority of current calibration methods for industrial robots only consider positional errors, neglecting the significance of orientation accuracy. This paper presents a more accurate error model and parameter calibration method for industrial robots based on six degrees-of-freedom position and orientation to identify the actual structural parameters. Firstly, based on the modified Denavit–Hartenberg parameters, the transformation errors of the tool coordinate system and measurement coordinate frame were introduced to establish a geometric parameter error model with positional and orientation accuracy as the optimization objectives. Secondly, to address the drawback of falling into local optima when identifying geometric parameters simultaneously, a geometric parameter cross-identification method based on the Levenberg–Marquardt algorithm is proposed. Lastly, the linear relationship between the parameters was analyzed, and a scheme for not calibrating some geometric parameters under specific conditions was given. Simulation results demonstrated that, under the premise of existing transformation errors, the proposed geometric parameter error model can accurately identify the actual structural parameters of industrial robots. After calibration, the positional error at the robot’s flange end decreased from 1.9536 mm to 0.0122 mm, and the orientation error decreased from 1.46 × 10−2 rad to 1.31 × 10−4 rad. Furthermore, compared to identifying the geometric parameters simultaneously, the proposed cross-identification method has a wider convergence range.

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Section: Introductionmentioning

confidence: 99%