Industrial Robot Calibration Using a Virtual Linear Constraint

Du, B. X.; Xi, Ning; Nieves, Erick

doi:10.21307/ijssis-2017-519

Cited by 10 publications

(10 citation statements)

References 18 publications

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“…value through RS232 , after MATLAB's calculation and analysis, put the various coordinate errors and the shaft angles' value into formula (11), (12), (13) for calculation, get the relationship between various parameters of the joint error.…”

Section: Experimental Platform and Methodsmentioning

confidence: 99%

“…Due to the fact that the dynamic error is generally nonlinear, in engineering practice, a variety of methods can be used to compensate dynamic error [9][10][11] . Among them, the BP network is widely used because of its strong nonlinear mapping ability in the error compensation, but the network also has disadvantages such as the learning process is long, low efficiency, easy to get local optima.…”

Section: Feasibility Of Online Compensationmentioning

confidence: 99%

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Error Analysis and Compensation Method of 6-axis Industrial Robot

Zhang¹,

Jinda²

2013

International Journal on Smart Sensing and Intelligent Systems

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Section: Experimental Platform and Methodsmentioning

confidence: 99%

Section: Feasibility Of Online Compensationmentioning

confidence: 99%

Error Analysis and Compensation Method of 6-axis Industrial Robot

Zhang¹,

Jinda²

2013

International Journal on Smart Sensing and Intelligent Systems

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“…They are electing upper, middle and lower three-point of the scan line through the agreed order, and get the cross product based on these three points to two vector quantities can determine the general direction of where the center of the ball. Thus we can remove the pseudo-solution [8].…”

Section: ) To Calibrate Rotational Relationship R S By Constraining mentioning

confidence: 99%

Error Modeling And Compensation Of 3D Scanning Robot System Based On Pso-Rbfnn

Jinda

2017

International Journal on Smart Sensing and Intelligent Systems

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In order to improve the measurement accuracy of three-dimensional (3D) scanning robot, a method of 3D scanning robot system error modeling and compensation based on particle swarm optimization radial basis function neural network (PSO-RBFNN) is proposed to achieve intelligent compensation of measurement error. The structure, calibration and error modeling process of 3D scanning robot system are mainly described. Cleverly using the iterative closest point (ICP) algorithm to construct input and output data pairs of neural network, and the specific process of error modeling using PSO-RBFNN is given. Finally, through the actual experiment we test and verify the correctness and effectiveness of the proposed error modeling and compensation method by measuring the distance between the centers of two standard balls. Experimental results show: the proposed error model and the compensation method can effectively compensate the measurement errors and improve the accuracy of the 3D scanning robot system.

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“…It has been proved that equation (8) is equivalent to the following perturbed linear program [13][14][15][16] :…”

Section: Basis Pursuit Dedoising (Bpdn)mentioning

confidence: 99%

Applications Of Compressive Sensing Over Wireless Fading Channels

Wang

Zhou

et al. 2014

International Journal on Smart Sensing and Intelligent Systems

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Abstract-Wireless fading channels exist mixed noise, the most common noises are gaussian noise and impulse noise, to ensure the quality of the received signal, removing the noise in the channel is very important and necessary. WSDN (wavelet soft-threshold de-noising) can suppress low-intensity gaussian noise well; unfortunately, the denoising effect in removing impulse noise using WSDN is not obvious, especially when noise intensity is relatively high. BPDN (Basis Pursuit Denoising) which is a reconstruction algorithm of compressive sensing can control high-intensity gaussian noise in the channels well preceding WSDN, but also the effect of BPDN denoising the common impulse noise is not obvious, to denoise impulse noise, we adopt IBPDN (Improved Basis Pursuit Denoising) that changes L2 form to noise to L1 form, which was proposed in 2006 by Guosheng Bing. The experimental data show that IBPDN has good anti-noise ability to both gaussian noise and impulse noise; furthermore, more satisfactory results are obtained using IBPDN to mixed noise than those with BPDN.

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Industrial Robot Calibration Using a Virtual Linear Constraint

Cited by 10 publications

References 18 publications

Error Analysis and Compensation Method of 6-axis Industrial Robot

Error Analysis and Compensation Method of 6-axis Industrial Robot

Error Modeling And Compensation Of 3D Scanning Robot System Based On Pso-Rbfnn

Applications Of Compressive Sensing Over Wireless Fading Channels

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