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

2019 ◽  
Vol 9 (9) ◽  
pp. 1940 ◽  
Author(s):  
Chi-Tho Cao ◽  
Van-Phu Do ◽  
Byung-Ryong Lee
Keyword(s):  
Neural Network ◽  
Position Error ◽  
Industrial Robots ◽  
Positional Error ◽  
Absolute Position ◽  
Position Errors ◽  
The Neural Network ◽  
Robot Positioning ◽  
The Absolute ◽  
Calibration Approach

It is well known that most of the industrial robots have excellent repeatability in positioning. However, the absolute position errors of industrial robots are relatively poor, and in some cases the error may reach even several millimeters, which make it difficult to apply the robot system to vehicle assembly lines that need small position errors. In this paper, we have studied a method to reduce the absolute position error of robots using machine vision and neural network. The position/orientation of robot tool-end is compensated using a vision-based approach combined with a neural network, where a novel indirect calibration approach is presented in order to gather information for training the neural network. In the simulation, the proposed compensation algorithm was found to reduce the positional error to 98%. On average, the absolute position error was 0.029 mm. The application of the proposed algorithm in the actual robot experiment reduced the error to 50.3%, averaging 1.79 mm.

Emulating and Modeling for Position Errors of Ultra-Precision Aspherical Grinding

2007 ◽  
Vol 10-12 ◽  
pp. 291-296
Author(s):  
Dong Ju Chen ◽  
Yong Zhang ◽  
Fei Hu Zhang ◽  
H.M. Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Motor Coordination ◽  
Error Model ◽  
Position Error ◽  
Numerical Control ◽  
Training Data ◽  
Position Errors ◽  
The Neural Network ◽  
Ultra Precision

In the process of the ultra-precision grinding, the machining path of the aspherical is the result of motor coordination by several axes for the numerical control system. Since the motion of each axis have errors, there are big errors between the real positions and the theoretical positions, and the position error of the wheel infects the accuracy of the workpiece greatly. This paper analyses the position error property of the wheel and finds the machining approach path has nothing to do with the position error, just do with to the present machining point. In order to solve the problem, the method using the Neural Network optimized by the Genetic Algorithm to establish the position error model is introduced. A three-layer error back propagation (simplified as BP) Neural Network is used to establish the position error model, the position coordinates (x, z) of the program instruction is input layer, and the corroding measured error value ( Δx , Δz ) is output layer. Before training data sample, using the Genetic Algorithm to optimize the Neural Network to improve the predicting accuracy of the Neural Network, and reduce the training time. The emulation results indicate that using the Neural Network model optimized by the Genetic Algorithm can predict the position error in a high degree of accuracy, and at the same time, according to the predicting results, compensating the position error of the wheel is possible.

A Hybrid Neural Network Approach for Increasing the Absolute Accuracy of Industrial Robots

2021 ◽  
Author(s):  
Christian Landgraf ◽  
Kilian Ernst ◽  
Gesine Schleth ◽  
Marc Fabritius ◽  
Marco F. Huber
Keyword(s):  
Neural Network ◽  
Industrial Robots ◽  
Absolute Accuracy ◽  
Network Approach ◽  
Neural Network Approach ◽  
Hybrid Neural Network ◽  
The Absolute

scholarly journals Adaptive-Cognitive Kalman Filter and Neural Network for an Upgraded Nondispersive Thermopile Device to Detect and Analyze Fusarium Spores

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4900
Author(s):  
Son Pham ◽  
Anh Dinh
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Background Noise ◽  
Thermal Noise ◽  
Absolute Error ◽  
Adaptive Kalman Filter ◽  
Training Time ◽  
Burst Noise ◽  
The Neural Network ◽  
The Absolute

Noises such as thermal noise, background noise or burst noise can reduce the reliability and confidence of measurement devices. In this work, a recursive and adaptive Kalman filter is proposed to detect and process burst noise or outliers and thermal noise, which are popular in electrical and electronic devices. The Kalman filter and neural network are used to preprocess data of three detectors of a nondispersive thermopile device, which is used to detect and quantify Fusarium spores. The detectors are broadband (1 µm to 20 µm), λ 1 (6.09 ± 0.06 µm) and λ 2 (9.49 ± 0.44 µm) thermopiles. Additionally, an artificial neural network (NN) is applied to process background noise effects. The adaptive and cognitive Kalman Filter helps to improve the training time of the neural network and the absolute error of the thermopile data. Without applying the Kalman filter for λ 1 thermopile, it took 12 min 09 s to train the NN and reach the absolute error of 2.7453 × 104 (n. u.). With the Kalman filter, it took 46 s to train the NN to reach the absolute error of 1.4374 × 104 (n. u.) for λ 1 thermopile. Similarly, to the λ 2 (9.49 ± 0.44 µm) thermopile, the training improved from 9 min 13 s to 1 min and the absolute error of 2.3999 × 105 (n. u.) to the absolute error of 1.76485 × 105 (n. u.) respectively. The three-thermopile system has proven that it can improve the reliability in detection of Fusarium spores by adding the broadband thermopile. The method developed in this work can be employed for devices that encounter similar noise problems.

Study of GPS Position Error during 16 June, 2005 (Disturb Day) at Bhopal

2017 ◽  
Vol SED2017 (01) ◽  
pp. 1-4
Author(s):  
Richa Trivedi
Keyword(s):  
Geomagnetic Latitude ◽  
Position Error ◽  
Source Mechanism ◽  
Ionospheric Scintillation ◽  
Positional Error ◽  
Gps Receiver ◽  
Error Ellipse ◽  
Equatorial Station ◽  
Position Errors ◽  
Level Of Confidence

In order to study GPS position error, the GPS Ionospheric Scintillation and TEC Monitor (GISTM) based GPS receiver was installed at an equatorial station, Bhopal (23.2° N, 77.4° E, Geomagnetic latitude 14.23˚ N), India. We analyzed the horizontal error and the level of confidence in terms of DRMS and CEP and positional error from fixed GPS point for 16 June 2005(disturb day). In order to study the effect of storm on GPS position errors, the latitudinal error and longitudinal error in meter is studied. We observed that the maximum number of error points in the latitudinal error lies between 1.95 to -1.57 meter while longitudinal error points lies between 1.09 to – 1.28 meter. It was observed that some of the error points lie out the 95% error ellipse and it is observed that the error point’s increased in N and N-E direction. The results have been compared with the earlier ones and discussed in terms of possible source mechanism responsible for the position error at anomaly crest region.

scholarly journals Position Estimation of Vehicle Based on Magnetic Marker: Time-Division Position Correction

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8274
Author(s):  
Yeun Sub Byun ◽  
Rag Gyo Jeong
Keyword(s):  
Correction Method ◽  
Position Estimation ◽  
Position Error ◽  
Absolute Position ◽  
Position Information ◽  
Magnetic Marker ◽  
Position Correction ◽  
The Absolute ◽  
Magnetic Markers ◽  
Time Division

During the automatic driving of a vehicle, the vehicle’s positional information is important for vehicle driving control. If fixed-point land markers such as magnetic markers are used, the vehicle’s current position error can be calculated only when a marker is detected while driving, and this error can be used to correct the estimation position. Therefore, correction information is used irregularly and intermittently according to the installation intervals of the magnetic markers and the driving speed. If the detected errors are corrected all at once using the position correction method, discontinuity of the position information can occur. This problem causes instability in the vehicle’s route guidance control because the position error fluctuates as the vehicle’s speed increases. We devised a time-division position correction method that calculates the error using the absolute position of the magnetic marker, which is estimated when the magnetic marker is detected, along with the absolute position information from the magnetic marker database. Instead of correcting the error at once when the position and heading errors are corrected, the correction is performed by dividing the errors multiple times until the next magnetic marker is detected. This prevents sudden discontinuity of the vehicle position information, and the calculated correction amount is used without loss to obtain stable and continuous position information. We conducted driving tests to compare the performances of the proposed algorithm and conventional methods. We compared the continuity of the position information and the mean error and confirmed the superiority of the proposed method in terms of these aspects.

A flexible calibration method connecting the joint space and the working space of industrial robots

2018 ◽  
Vol 45 (3) ◽  
pp. 407-415 ◽  
Author(s):  
Ying Cai ◽  
Peijiang Yuan ◽  
Dongdong Chen
Keyword(s):  
Industrial Robot ◽  
Calibration Method ◽  
Joint Space ◽  
Position Error ◽  
Industrial Robots ◽  
Content Type ◽  
Position Errors ◽  
Working Space ◽  
Calibration Experiment ◽  
Control Command

Purpose To improve the accuracy of the industrial robots’ absolute positioning, a Kriging calibration is proposed. Design/methodology/approach This method particularly designs a semivariogram for connecting the joint space and the working space. After that, Kriging equations are determined and solved to predict the position errors of targets. Subsequently, a simple and convenient error compensation, which can be implemented on the control command, is proposed. Findings The verification experiment of the position-error multiplicity and the Kriging calibration experiment are done in the KUKA R210 R2700 industrial robot. The position-error multiplicity experiment reveals that the position error of the industrial robot varies with the joint angle sets. Besides, the Kriging calibration experiment shows that the maximum of the spatial position errors is reduced from 1.2906 to 0.2484 mm, which reveals the validity of the Kriging calibration. Originality/value The special designed semivariation allows this method to be flexible and practical. It can be used in various fields where the angle solutions of industrial robots should be adapted according to the optimal demand and the environment, such as the optimal trajectory planning and the obstacle avoidance. Besides, this method can provide accuracy positioning results.

scholarly journals Dynamic Modeling and Adaptive Controlling in GPS-Intelligent Buoy (GIB) Systems Based on Neural-Fuzzy Networks

2020 ◽  
Author(s):  
Dangquan Zhang ◽  
Muhammad Aqeel Ashraf ◽  
Zhenling Liu ◽  
Wan-Xi Peng ◽  
Mohammad Javad Golkar ◽  
...  
Keyword(s):  
Neural Network ◽  
Intelligent Systems ◽  
System Modeling ◽  
Adaptive Fuzzy Neural Network ◽  
Position Errors ◽  
The Neural Network ◽  
Proposed Model ◽  
Fuzzy Neural ◽  
Dynamic System Modeling ◽  
And Control

Recently, various relations and criteria have been presented to establish a proper relationship between control systems and control Global Positioning System (GPS)-intelligent buoy system. Given the importance of controlling the position of buoys and the construction of intelligent systems, in this paper, dynamic system modeling is applied to position marine buoys through the improved neural network with a backstepping technique. This study aims at developing a novel controller based on adaptive fuzzy neural network to optimally track the dynamically positioned vehicle on water with unavailable velocities and unidentified control parameters. In order to model the network with the proposed technique, uncertainties and the unwanted disturbances are studied in the neural network. The presented study aims at developing a neural controlling which applies the vectorial back-stepping technique to the surface ships, which have been dynamically positioned with undetermined disturbances and ambivalences. Moreover, the objective function is to minimize the output error for the neural network (NN) based on closed-loop system. The most important feature of the proposed model for the positioning buoys is its independence from comparative knowledge or information on the dynamics and the unwanted disturbances of ships. The numerical and obtained consequences demonstrate that the controller system can adjust the routes and the position of the buoys to the desired objective with relatively few position errors.

Statistical Characteriztion of Position Errors of an Ensemble of Robots and Its Applications

1989 ◽  
Vol 111 (2) ◽  
pp. 215-222 ◽  
Author(s):  
Chia-Hsiang Menq ◽  
Jin-Hwan Borm
Keyword(s):  
Reference Frames ◽  
Statistical Error ◽  
Position Error ◽  
Positioning Accuracy ◽  
Position Accuracy ◽  
Position Errors ◽  
Working Space ◽  
Absolute Positioning ◽  
Error Field ◽  
The Absolute

For the accurate control and implementation of a robot in an integrated manufacturing environment using off-line programming, a knowledge of the absolute positioning accuracy of the robot becomes important. This paper presents a framework which can be used to statistically represent the absolute positioning accuracy for a family of robots. Statistical error measure indices are proposed to represent the position error field over the working space for a family of robots. This error field provides statistical information for the position errors of the end-effector and can be a guide for the determination of the optimal design tolerances of the parts composing of a robot. The second objective of the paper is to introduce a simple interpolation scheme to improve the local position accuracy by teaching one or more task reference frames with which goal positions are mathematically expressed. It will be shown how the method shifts or alters the position error field in order to maintain the desired position accuracy within a desired working area.

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