Localization of Mobile Robot Based on Least Square Method

2013 ◽  
Vol 347-350 ◽  
pp. 808-811
Author(s):  
Jia Lu Li ◽  
Lin Bing Long ◽  
Bao Feng Zhang
Keyword(s):  
Motion Estimation ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Least Squares ◽  
Least Square Method ◽  
Least Square ◽  
Experimental Results ◽  
Sift Algorithm ◽  
Key Techniques ◽  
Point Detection

Localization is the basis for navigation of mobile robots. This paper focuses on key techniques of localization for mobile robots based on vision. Firstly, the specific measures and steps of the algorithm are analyzed and researched in depth. In the study, SIFT algorithm combined with epipolar geometry constraint is used on the environment feature point detection, matching and tracking. And the method of RANSAC combined with the least squares is used to obtain accurate results of the motion estimation. Then the necessary experiments are carried out to verify the correctness and effectiveness of algorithms. The experimental results verified the accuracy of the improved algorithm.

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.

Fuzzy C-Regression Models

2013 ◽  
Vol 278-280 ◽  
pp. 1323-1326
Author(s):  
Yan Hua Yu ◽  
Li Xia Song ◽  
Kun Lun Zhang
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Linear Models ◽  
Least Squares Method ◽  
Least Square Method ◽  
Least Square ◽  
Estimation Methods ◽  
Statistical Regression ◽  
Fuzzy Linear Regression ◽  
Fuzzy Degree

Fuzzy linear regression has been extensively studied since its inception symbolized by the work of Tanaka et al. in 1982. As one of the main estimation methods, fuzzy least squares approach is appealing because it corresponds, to some extent, to the well known statistical regression analysis. In this article, a restricted least squares method is proposed to fit fuzzy linear models with crisp inputs and symmetric fuzzy output. The paper puts forward a kind of fuzzy linear regression model based on structured element, This model has precise input data and fuzzy output data, Gives the regression coefficient and the fuzzy degree function determination method by using the least square method, studies the imitation degree question between the observed value and the forecast value.

Determination of Johnson Cook Material Model Constants and Their Influence on Machining Simulations of Tungsten Heavy Alloy

2018 ◽  
Author(s):  
Chithajalu Kiran Sagar ◽  
Amrita Priyadarshini ◽  
Amit Kumar Gupta ◽  
Sidharth Kumar Shukla
Keyword(s):  
Least Square Method ◽  
Material Model ◽  
Least Square ◽  
Experimental Results ◽  
Tungsten Heavy Alloy ◽  
Fe Model ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Aircraft Manufacturing ◽  
Heavy Alloys

Tungsten Heavy Alloys (WHA) are used in counterbalance and ballast weights for aerodynamic balancing in fixed and rotary wing aircraft. Manufacturing these components for closer tolerances using machining is a challenging task. The present work aims to develop a 2D Finite Element (FE) model to simulate the chip formation process during machining of WHA using Johnson Cook Material Model (JCMM). The model constants for 95%WHA are determined based on the high strain rate test data using least square method. The calculated values are further optimized using Genetic Algorithm (GA) and Artificial Bee Colony (ABC) algorithm, which are then used as material inputs for FE simulation of machining WHA. The predicted results such as cutting force, chip geometry, shear stress, shear angle are presented and compared with the experimental results under similar cutting conditions. It has been observed that the constants obtained from ABC algorithm show minimum error in the cutting performance measures for all the experimental results.

scholarly journals Furniture Model Creation Through Direct Teaching to a Mobile Robot

2008 ◽  
Vol 20 (2) ◽  
pp. 213-220 ◽  
Author(s):  
Kimitoshi Yamazaki ◽  
◽  
Takashi Tsubouchi ◽  
Masahiro Tomono ◽  
◽  
...  
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Real Life ◽  
Experimental Results ◽  
Sensor Data ◽  
Small Object ◽  
Laser Range Finder ◽  
Range Finder ◽  
Laser Range ◽  
Direct Teaching

In this paper, a modeling method to handle furniture is proposed. Real-life environments are crowded with objects such as drawers and cabinets that, while easily dealt with by people, present mobile robots with problems. While it is to be hoped that robots will assist in multiple daily tasks such as putting objects in into drawers, the major problems lies in providing robots with knowledge about the environment efficiently and, if possible, autonomously.If mobile robots can handle these furniture autonomously, it is expected that multiple daily jobs, for example, storing a small object in a drawer, can be performed by the robots. However, it is a perplexing process to give several pieces of knowledge about the furniture to the robots manually. In our approach, by utilizing sensor data from a camera and a laser range finder which are combined with direct teaching, a handling model can be created not only how to handle the furniture but also an appearance and 3D shape. Experimental results show the effectiveness of our methods.

Solitary waves of the RLW equation via least squares method

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ozlem Ersoy Hepson ◽  
Idris Dag ◽  
Bülent Saka ◽  
Buket Ay
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Least Square Method ◽  
Least Square ◽  
Spline Functions ◽  
Algebraic Equations ◽  
B Spline ◽  
B Splines ◽  
Rlw Equation ◽  
Set Up

Abstract Integration using least squares method in space and Crank–Nicolson approach in time is managed to set up an algorithm to solve the RLW equation numerically. Trial functions in the least square method consist of a combination of the quartic B-spline functions. Integration of the RLW equation gives a system of algebraic equations. The solutions consisting of a combination of the quartic B-splines are given for some initial and boundary value problems of RLW equation.

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 New Method to Identify the Preisach Distribution Function of Hysteresis

2005 ◽  
Vol 475-479 ◽  
pp. 2107-2110 ◽  
Author(s):  
Fan Li ◽  
Jian Qin Mao ◽  
Hai Shan Ding ◽  
Wen Bo Zhang ◽  
Hui Bin Xu ◽  
...  
Keyword(s):  
Distribution Function ◽  
Least Squares ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Least Squares Method ◽  
Input Sequence ◽  
Least Square Method ◽  
Least Square ◽  
New Method ◽  
Inference System

In this paper, a new method which combines the least square method with Tree-Structured fuzzy inference system is presented to approximate the Preisach distribution function. Firstly, by devising the input sequence and measure the output, discrete Preisach measure can be identified by the use of the least squares method. Then, the Preisach function can be obtained with Tree-Structured fuzzy inference system without any special smoothing means. So, this new method is not sensitive to noise, and is a universal approximator of the Preisach function. It collect the merit and overcome the deficiency of the existing methods.

Combined Phase Unwrapping Method Using 2D Image Information in PMP 3D Measurement

2013 ◽  
Vol 765-767 ◽  
pp. 2148-2152 ◽  
Author(s):  
Yue Hua Gao ◽  
Bing Luo ◽  
Zhong Yu Sun ◽  
Su Fang Zhao
Keyword(s):  
Basic Principle ◽  
Least Square Method ◽  
Phase Unwrapping ◽  
Least Square ◽  
Experimental Results ◽  
Solder Paste ◽  
3D Measurement ◽  
Image Information ◽  
Practical Engineering ◽  
Break Area

In SMT solder paste deposit 3D measurement based on PMP, conventional phase unwrapping method suffered from shadow, noise and holes. Considering practical engineering condition, phase unwrapping method can be improved using 2D image information: basic principle method was applied to smooth area, quality-guided unwrapping and least-square method were taken in shadow or break area for phase unwrapping respectively. Experimental results show that desired phases can be correctly and quickly unwrapped in proposed method.

scholarly journals Time Scale in Least Square Method

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Özgür Yeniay ◽  
Öznur İşçi ◽  
Atilla Göktaş ◽  
M. Niyazi Çankaya
Keyword(s):  
Least Squares ◽  
Time Scale ◽  
Least Squares Method ◽  
Least Square Method ◽  
Forward Direction ◽  
Ordinary Least Squares ◽  
Least Square ◽  
Regression Equations ◽  
Nabla Derivative ◽  
Derivative Concept

Study of dynamic equations in time scale is a new area in mathematics. Time scale tries to build a bridge between real numbers and integers. Two derivatives in time scale have been introduced and called as delta and nabla derivative. Delta derivative concept is defined as forward direction, and nabla derivative concept is defined as backward direction. Within the scope of this study, we consider the method of obtaining parameters of regression equation of integer values through time scale. Therefore, we implemented least squares method according to derivative definition of time scale and obtained coefficients related to the model. Here, there exist two coefficients originating from forward and backward jump operators relevant to the same model, which are different from each other. Occurrence of such a situation is equal to total number of values of vertical deviation between regression equations and observation values of forward and backward jump operators divided by two. We also estimated coefficients for the model using ordinary least squares method. As a result, we made an introduction to least squares method on time scale. We think that time scale theory would be a new vision in least square especially when assumptions of linear regression are violated.

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