A Novel Indirect Calibration Approach for Robot Positioning Error Compensation Based on Neural Network and Hand-Eye Vision

Cao, Chi-Tho; Do, Van-Phu; Lee, Byung-Ryong

doi:10.3390/app9091940

Cited by 28 publications

(13 citation statements)

References 23 publications

(28 reference statements)

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“…Cao et al [4] studied a method of using machine vision and a neural network (NN) to decrease the absolute position error of the robot. However, the NN was easily trapped in a local solution, which led to instability in the localization accuracy.…”

Section: Introductionmentioning

confidence: 99%

Random Target Localization for an Upper Limb Prosthesis

Zhang

Fan

Wang

et al. 2021

Shock and Vibration

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To achieve the purpose of accurately grasping a random target with the upper limb prosthesis, the acquisition of target localization information is especially important. For this reason, a novel type of random target localization algorithm is proposed. Firstly, an initial localization algorithm (ILA) that uses two 3D attitude sensors and a laser range sensor to detect the target attitude and distance is presented. Secondly, an error correction algorithm where a multipopulation genetic algorithm (MPGA) optimizes backpropagation neural network (BPNN) is utilized to improve the accuracy of ILA. Thirdly, a general regression neural network (GRNN) algorithm is proposed to calculate the joint angles, which are used to control the upper limb prosthetic gripper to move to the target position. Finally, the proposed algorithm is applied to the 5-DOF upper limb prosthesis, and the simulations and experiments are proved to demonstrate the validity of the proposed localization algorithm and inverse kinematics (IK) algorithm.

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Section: Introductionmentioning

confidence: 99%

Random Target Localization for an Upper Limb Prosthesis

Zhang

Fan

Wang

et al. 2021

Shock and Vibration

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“…There are many measurement tools that are used in machine tools and robot calibration, such as laser interferometer, laser ball bar, ball-column, laser tracker, optical CMM and visual system [20][21][22][23]. An et al [20] use a laser tracker calibration and compensation of machine tool volumetric error.…”

Section: Introductionmentioning

confidence: 99%

“…An et al [20] use a laser tracker calibration and compensation of machine tool volumetric error. Cao et al [21] have studied a method to reduce and compensate the position error of robot using machine vision combined with a neural network. Albert et al [22] present a technique used to find the 29 error parameters of an ABB IRB 1600 robot that best fit the measures acquired with a laser tracker.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Joint Assembly Geometric Errors Affecting End-Effector for Six-Axis Robots

2020

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This paper presents an analysis of the geometric errors of joint assembly that affect the end-effector for a six-axis industrial robot. The errors were composed of 30 parameters that come from the Geometric Dimensioning and Tolerancing (GD&T) design, which is not the normal way to describe them. Three types of manufacturing tolerancing—perpendicularity, parallelism and position—were introduced and investigated. These errors were measured by the laser tracker. The measurement data were calculated with an analysis of the circle fitting method. The kinematic model and error model based on a combination of translations methods were used. The experiment was carried out in order to calculate the tolerancing of geometric error. Then, the positions of the end-effector in the actual measurement from laser tracker and exact performance were compared. The discrepancy was compensated by offline programming. As a result, the position errors were reduced by 90%.

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“…Thanks to its principle of data driven modeling, the artificial neural network (ANN) has a promising application in modeling complex systems such as calibration and error compensation of industrial robots [13][14][15][16]. However, one of the drawbacks of ANN for robot error calibration is the uncertainty of error sources.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Calibration of a Heavy-Duty Mechanical Arm in Coal Mine

et al. 2020

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Accurate positioning of an airborne heavy-duty mechanical arm in coal mine, such as a roof bolter, is important for the efficiency and safety of coal mining. Its positioning accuracy is affected not only by geometric errors but also by nongeometric errors such as link and joint compliance. In this paper, a novel calibration method based on error limited genetic algorithm (ELGA) and regularized extreme learning machine (RELM) is proposed to improve the positioning accuracy of a roof bolter. To achieve the improvement, the ELGA is firstly implemented to identify the geometric parameters of the roof bolter’s kinematics model. Then, the residual positioning errors caused by nongeometric facts are compensated with the regularized extreme learning machine (RELM) network. Experiments were carried out to validate the proposed calibration method. The experimental results show that the root mean square error (RMSE) and the mean absolute error (MAE) between the actual mast end position and the nominal mast end position are reduced by more than 78.23%. It also shows the maximum absolute error (MAXE) between the actual mast end position and the nominal mast end position is reduced by more than 58.72% in the three directions of Cartesian coordinate system.

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A Novel Indirect Calibration Approach for Robot Positioning Error Compensation Based on Neural Network and Hand-Eye Vision

Cited by 28 publications

References 23 publications

Random Target Localization for an Upper Limb Prosthesis

Random Target Localization for an Upper Limb Prosthesis

An Analysis of Joint Assembly Geometric Errors Affecting End-Effector for Six-Axis Robots

Intelligent Calibration of a Heavy-Duty Mechanical Arm in Coal Mine

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