Kalman Filter based position estimation using "optical mouse movement sensor" and differential drive robot model

2014 ◽  
Author(s):  
Gyorgy Csaba ◽  
Zoltan Vamossy
Keyword(s):  
Kalman Filter ◽  
Position Estimation ◽  
Differential Drive ◽  
Mouse Movement ◽  
Differential Drive Robot ◽  
Optical Mouse

An Autonomously Guided Differential Drive Robot Base Using Asus® Xtion Pro Live

2020 ◽  
Author(s):  
S. I. A. P. Diddeniya ◽  
J. Liyanage ◽  
W. K. I. L. Wanniarachchi ◽  
C. Premachandra ◽  
H. N. Gunasinghe
Keyword(s):  
Differential Drive ◽  
Differential Drive Robot

Minimum Energy Control of a Unicycle Model Robot

2021 ◽  
Author(s):  
Youngjin Kim ◽  
Tarunraj Singh
Keyword(s):  
Optimal Control ◽  
Minimum Energy ◽  
Kinematic Model ◽  
Elliptic Functions ◽  
Terminal Point ◽  
Jacobi Elliptic Functions ◽  
Differential Drive ◽  
Terminal Constraints ◽  
Differential Drive Robot ◽  
Point To Point

Abstract Point-to-point path planning for a kinematic model of a differential-drive wheeled mobile robot (WMR) with the goal of minimizing input energy is the focus of this work. An optimal control problem is formulated to determine the necessary conditions for optimality and the resulting two point boundary value problem is solved in closed form using Jacobi elliptic functions. The resulting nonlinear programming problem is solved for two variables and the results are compared to the traditional shooting method to illustrate that the Jacobi elliptic functions parameterize the exact profile of the optimal trajectory. A set of terminal constraints which lie on a circle in the first quadrant are used to generate a set of optimal solutions. It is noted that for maneuvers where the angle of the vector connecting the initial and terminal point is greater than a threshold, which is a function of the radius of the terminal constraint circle, the robot initially moves into the third quadrant before terminating in the first quadrant. The minimum energy solution is compared to two other optimal control formulations: (1) an extension of the Dubins vehicle model where the constant linear velocity of the robot is optimized for and (2) a simple turn and move solution, both of whose optimal paths lie entirely in the first quadrant. Experimental results are used to validate the optimal trajectories of the differential-drive robot.

scholarly journals GPS Receiver Position Augmentation Using Correntropy Kalman Filter in Low Latitude Terrain

2022 ◽  
Author(s):  
Sirish Kumar Pagoti ◽  
Bala Sai Srilatha Indira Dutt Vemuri ◽  
Ganesh Laveti
Keyword(s):  
Kalman Filter ◽  
Indian Subcontinent ◽  
Estimation Algorithm ◽  
Position Estimation ◽  
Gps Receiver ◽  
Dual Frequency ◽  
Low Latitude ◽  
Position Accuracy ◽  
Aircraft Landing ◽  
Gps Accuracy

If any Global Positioning System (GPS) receiver is operated in low latitude regions or urban canyons, the visibility further reduces. These system constraints lead to many challenges in providing precise GPS position accuracy over the Indian subcontinent. As a result, the standalone GPS accuracy does not meet the aircraft landing requirements, such as Category I (CAT-I) Precision Approaches. However, the required accuracy can be achieved by augmenting the GPS. Among all these issues, the predominant factors that significantly influence the receiver position accuracy are selecting a user/receiver position estimation algorithm. In this article, a novel method is proposed based on correntropy and designated as Correntropy Kalman Filter (CKF) for precise GPS applications and GPS Aided Geosynchronous equatorial orbit Augmented Navigation (GAGAN) based aircraft landings over the low latitude Indian subcontinent. The real-world GPS data collected from a dual-frequency GPS receiver located in the southern region of the Indian subcontinent (IISc), Bangalore with Lat/Long: 13.021°N/ 77.5°E) is used for the performance evaluation of the proposed algorithm. Results prove that the proposed CKF algorithm exhibits significant improvement (up to 34%) in position estimation compared to the traditional Kalman Filter.

scholarly journals Extended Kalman Filter (EKF) Design for Vehicle Position Tracking Using Reliability Function of Radar and Lidar

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4126 ◽  
Author(s):  
Taeklim Kim ◽  
Tae-Hyoung Park
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Extended Kalman Filter ◽  
Reliability Function ◽  
Position Estimation ◽  
Position Tracking ◽  
Radar Sensors ◽  
Vehicle Position ◽  
Sensor Errors ◽  
Fuzzy Adaptive

Detection and distance measurement using sensors is not always accurate. Sensor fusion makes up for this shortcoming by reducing inaccuracies. This study, therefore, proposes an extended Kalman filter (EKF) that reflects the distance characteristics of lidar and radar sensors. The sensor characteristics of the lidar and radar over distance were analyzed, and a reliability function was designed to extend the Kalman filter to reflect distance characteristics. The accuracy of position estimation was improved by identifying the sensor errors according to distance. Experiments were conducted using real vehicles, and a comparative experiment was done combining sensor fusion using a fuzzy, adaptive measure noise and Kalman filter. Experimental results showed that the study’s method produced accurate distance estimations.

scholarly journals Research on sensorless control method of permanent magnet synchronous motor based on iterated cubature Kalman filter

2019 ◽  
Vol 103 (1) ◽  
pp. 003685041989027
Author(s):  
Shi Peicheng ◽  
Wang Chen ◽  
Zhang Rongyun ◽  
Wang Suo
Keyword(s):  
Kalman Filter ◽  
Permanent Magnet ◽  
Sensorless Control ◽  
Permanent Magnet Synchronous Motor ◽  
Estimation Method ◽  
Synchronous Motor ◽  
Position Estimation ◽  
Motor Speed ◽  
Rotor Position ◽  
Cubature Kalman Filter

Aiming at the problems of high cost, increased volume, low reliability, and environmental interference caused by sensor installation on permanent magnet synchronous motor, estimation method for motor speed and rotor position is proposed based on iterated cubature Kalman filter algorithm and applied to permanent magnet synchronous motor sensorless control. First, discrete mathematical model of permanent magnet synchronous motor in α-β coordinate system is established. Then, based on cubature Kalman filter and iterated cubature Kalman filter, simulation model of sensorless vector control system with dual closed-loop of permanent magnet synchronous motor speed and current is established. Also, simulation verification of two working conditions with given rotation speed and load is carried out. Finally, hardware experimental verification platform is built based on TMS320F28335 chip. Both simulation analysis and experimental results show that iterated cubature Kalman filter application to sensorless control of permanent magnet synchronous motor demonstrates good anti-load variation interference, stable motor operation, high motor speed and rotor position estimation accuracy, which suits the application with high requirement for precise motor control and mean important reference value and promotion significance.

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