scholarly journals Design of Jitter Compensation Algorithm for Robot Vision Based on Optical Flow and Kalman Filter

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
B. R. Wang ◽  
Y. L. Jin ◽  
D. L. Shao ◽  
Y. Xu
Keyword(s):  
Kalman Filter ◽  
Optical Flow ◽  
Robot Vision ◽  
Least Square Method ◽  
Coefficient Matrix ◽  
Least Square ◽  
Image Motion ◽  
Point Pair ◽  
Eigen Value ◽  
Motion Parameters

Image jitters occur in the video of the autonomous robot moving on bricks road, which will reduce robot operation precision based on vision. In order to compensate the image jitters, the affine transformation kinematics were established for obtaining the six image motion parameters. The feature point pair detecting method was designed based on Eigen-value of the feature windows gradient matrix, and the motion parameters equation was solved using the least square method and the matching point pairs got based on the optical flow. The condition number of coefficient matrix was proposed to quantificationally analyse the effect of matching errors on parameters solving errors. Kalman filter was adopted to smooth image motion parameters. Computing cases show that more point pairs are beneficial for getting more precise motion parameters. The integrated jitters compensation software was developed with feature points detecting in subwindow. And practical experiments were conducted on two mobile robots. Results show that the compensation costing time is less than frame sample time and Kalman filter is valid for robot vision jitters compensation.

scholarly journals Performance evaluation of GPS / BDS combined single-point positioning based on RTKLIB

2020 ◽  
Vol 165 ◽  
pp. 03009
Author(s):  
Li Yan-yi ◽  
Huang Jin ◽  
Tang Ming-xiu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Single Point ◽  
Least Square Method ◽  
Least Square ◽  
Filter Method ◽  
Single System ◽  
Positioning Error ◽  
Dual Mode ◽  
Kalman Filter Method

In order to evaluate the performance of GPS / BDS, RTKLIB, an open-source software of GNSS, is used in this paper. In this paper, the least square method, the weighted least square method and the extended Kalman filter method are respectively applied to BDS / GPS single system for data solution. Then, the BDS system and GPS system are used for fusion positioning and the positioning results of the two systems are compared with that of the single system. Through the comparison of experiments, on the premise of using the extended Kalman filter method for positioning, when the GPS signal is not good, BDS data is introduced for dual-mode positioning, the positioning error in e direction is reduced by 36.97%, the positioning error in U direction is reduced by 22.95%, and the spatial positioning error is reduced by 16.01%, which further reflects the advantages of dual-mode positioning in improving a system robustness and reducing the error.

scholarly journals Online Parameter Identification and State of Charge Estimation of Battery Based on Multitimescale Adaptive Double Kalman Filter Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Wenxian Duan ◽  
Chuanxue Song ◽  
Yuan Chen ◽  
Feng Xiao ◽  
Silun Peng ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Unscented Kalman Filter ◽  
Least Square Method ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery Life ◽  
Model Parameters ◽  
Simulation Results

An accurate state of charge (SOC) can provide effective judgment for the BMS, which is conducive for prolonging battery life and protecting the working state of the entire battery pack. In this study, the first-order RC battery model is used as the research object and two parameter identification methods based on the least square method (RLS) are analyzed and discussed in detail. The simulation results show that the model parameters identified under the Federal Urban Driving Schedule (HPPC) condition are not suitable for the Federal Urban Driving Schedule (FUDS) condition. The parameters of the model are not universal through the HPPC condition. A multitimescale prediction model is also proposed to estimate the SOC of the battery. That is, the extended Kalman filter (EKF) is adopted to update the model parameters and the adaptive unscented Kalman filter (AUKF) is used to predict the battery SOC. The experimental results at different temperatures show that the EKF-AUKF method is superior to other methods. The algorithm is simulated and verified under different initial SOC errors. In the whole FUDS operating condition, the RSME of the SOC is within 1%, and that of the voltage is within 0.01 V. It indicates that the proposed algorithm can obtain accurate estimation results and has strong robustness. Moreover, the simulation results after adding noise errors to the current and voltage values reveal that the algorithm can eliminate the sensor accuracy effect to a certain extent.

Error Model and Calibration Analysis of the Flexible Coordinate Measuring Machine

2011 ◽  
Vol 121-126 ◽  
pp. 3273-3277 ◽  
Author(s):  
Fang Li ◽  
Shu Gui Liu ◽  
Lei Zhao
Keyword(s):  
Mathematical Model ◽  
Coordinate Measuring Machine ◽  
Least Square Method ◽  
Coefficient Matrix ◽  
Error Model ◽  
Least Square ◽  
Parameter Calibration ◽  
Measuring Machine ◽  
Improving Accuracy ◽  
The Mathematical Model

A new 5-DOF flexible coordinate measuring machine (CMM) is introduced in this paper, which uses REVO system produced by Renishaw. According to the D-H method, the mathematical model is built, and then the error model of the flexible CMM is derived. The parameter calibration based on the nonlinear least square method is analyzed theoretically. Due to the disadvantages of Gauss-Newton method, LM method is researched, which improved the singularity of the coefficient matrix. The calibration analysis is a basis for improving accuracy of the flexible CMM.

Comparing of Coordinate Transformation Based on Total Least Squares and Least Squares

2014 ◽  
Vol 522-524 ◽  
pp. 1211-1214
Author(s):  
Qing Wu Meng ◽  
Lu Meng
Keyword(s):  
Least Squares ◽  
Coordinate Transformation ◽  
Least Square Method ◽  
Coefficient Matrix ◽  
Total Least Squares ◽  
Least Square ◽  
Experimental Results ◽  
Transformation Models ◽  
Total Least Square

The coordinate transformation models based on least square method and total least square are built and discussed. The least square model only includes the errors of observation vectors, the total least square model simultaneously takes into consideration to the errors of observation vectors and the errors of coefficient matrix. The both models are verified and compared in experiment. The experimental results showed that the model of total least square is more in line with actual, and more reasonable than by least square theoretically, and the coordinate transformation solution result of total least square with least square is more near.

The State of Charge Estimation of Lithium Battery in Electric Vehicle Based on Extended Kalman Filter

2014 ◽  
Vol 953-954 ◽  
pp. 796-799
Author(s):  
Huan Huan Sun ◽  
Jun Bi ◽  
Sai Shao
Keyword(s):  
Kalman Filter ◽  
Electric Vehicle ◽  
Extended Kalman Filter ◽  
Nonlinear Problems ◽  
Least Square Method ◽  
State Equation ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery State Of Charge

Accurate estimation of battery state of charge (SOC) is important to ensure operation of electric vehicle. Since a nonlinear feature exists in battery system and extended kalman filter algorithm performs well in solving nonlinear problems, the paper proposes an EKF-based method for estimating SOC. In order to obtain the accurate estimation of SOC, this paper is based on composite battery model that is a combination of three battery models. The parameters are identified using the least square method. Then a state equation and an output equation are identified. All experimental data are collected from operating EV in Beijing. The results of the experiment show  that the relative error of estimation of state of charge is reasonable, which proves this method has good estimation performance.

Adaptive Modeling of Laser Powder Deposition Process for Control and Monitoring Application

2005 ◽  
Author(s):  
Mohammad Durali ◽  
Alireza Fathi ◽  
Amir Khajepour ◽  
Ehsan Toyserkani
Keyword(s):  
Kalman Filter ◽  
System Identification ◽  
Least Square Method ◽  
Deposition Process ◽  
Least Square ◽  
Filter Method ◽  
Recursive Least Square ◽  
Noise Elimination ◽  
Laser Powder Deposition ◽  
Powder Deposition

Laser Powder Deposition technique is an advanced production method with many applications. Despite this fact, reliable and accurate control schemes have not yet fully developed for this method. This article presents method for in time identification of the process for modeling and adaptation of proper control strategy. ARMAX structure is chosen for system model. Recursive least square method and Kalman Filter methods are adopted for system identification, and their performance are compared. Experimental data was used for system identification, and proper filtering schemes are devised here for noise elimination and increased estimation results. It was concluded that although both methods yield efficient performance and accurate results, Kalman Filter method gives better results in parameter estimations. The comparison of the results shows that this method can be used very efficiently in control and monitoring of Laser Powder Deposition process.

Investigation on adaptive method of torque characteristic in dual clutch transmission during launch phase

2020 ◽  
Vol 234 (3) ◽  
pp. 568-584
Author(s):  
Hongtao Hao ◽  
Tongli Lu ◽  
Jianwu Zhang ◽  
Wenjie Ding
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Sliding Friction ◽  
Least Square Method ◽  
Adaptive Method ◽  
Least Square ◽  
Dual Clutch Transmission ◽  
Engine Torque ◽  
Simulation And Experiment ◽  
Application Potential

To reduce sliding friction work and clutch judder, an adaptive method of torque characteristic in dual clutch transmission during launch phase is presented in this paper. The proposed approach provides a tool to identify the change of torque characteristic and adapt it in real time. Firstly, to reduce the influence of the error between the nominal engine torque and the actual engine torque, the estimator based on the extended Kalman filter is designed to estimate the transmitted torque of the dual-clutch transmission during launch phase. Furthermore, the torque characteristic adaptive method is presented by a combination of the estimator with the improved Least Square Method. Then, based on the established driveline model of the dual-clutch transmission, the torque characteristic adaptive method during launch phase is validated by MATLAB/Simulink. Finally, in order to further evaluate the application potential of the adaptive method, the experiments are conducted on a production vehicle equipped with the wet dual-clutch transmission. The simulation and experiment results show that the proposed algorithms work well.

Signal Conditioning of Low-Cost Gyroscope Using Kalman Filter and Nonlinear Least Square Method

2012 ◽  
Vol 622-623 ◽  
pp. 1519-1523
Author(s):  
C. Saraporn ◽  
T. Dolwichai ◽  
J. Srisertpol ◽  
K. Teeka
Keyword(s):  
Kalman Filter ◽  
Motion Control ◽  
Low Cost ◽  
Least Square Method ◽  
Scaling Factor ◽  
Least Square ◽  
Experimental Results ◽  
Signal Conditioning

Gyroscopes are important sensors in motion control in equipment such as airplanes, missiles and Segway. Low-cost gyroscopes have problems in signals such as bias, noise and scaling factor that decrease the efficiency of motion control. Therefore this paper is to present signal conditioning of low-cost gyroscopes using a Kalman filter to remove unwanted noise and nonlinear least square method to estimate parameters for compensation errors to the model by comparison with the encoder. The experimental results is shown that Kalman filter and nonlinear least square method can be used in signal conditioning of low-cost gyroscope for a more accurate signal.

A high precision indoor positioning method based on UKF

2021 ◽  
pp. 1-14
Author(s):  
Mujie Zhao ◽  
Tao Zhang ◽  
Di Wang
Keyword(s):  
Kalman Filter ◽  
Nonlinear Filtering ◽  
Unscented Kalman Filter ◽  
Least Square Method ◽  
Wide Band ◽  
High Order ◽  
Least Square ◽  
Time Of Arrival ◽  
State Variables ◽  
Fifth Order

Aiming at the nonlinear filter problem in Ultra Wide Band (UWB) navigation and position, a high-order Unscented Kalman Filter (UKF) position method is proposed. On the one hand, the position and velocity are used as state variables to establish a nonlinear filtering model based on UWB position system. On the other hand, based on the fifth order cubature transform (CT), the analytical solution of the high-order unscented Kalman filter is obtained by introducing a free parameter δ. To verify the effectiveness of the proposed method, the Time of Arrival (TOA) location method, the least square method and fifth order CKF method are introduced as comparison methods. The simulation and experimental results show that the proposed high-order UKF method has good positioning accuracy in both static and dynamic UWB positioning methods.

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