scholarly journals Improvement of Auto-Tracking Mobile Robot based on HSI Color Model

2018 ◽  
Vol 12 (3) ◽  
pp. 1349 ◽  
Author(s):  
Suresh Sundarajoo ◽  
Ahmad Shahrizan Abdul Ghani
Keyword(s):  
Mobile Robot ◽  
Light Level ◽  
Object Identification ◽  
Color Model ◽  
Vision Sensor ◽  
Image Processing Algorithm ◽  
Filtering Algorithm ◽  
Dc Motors ◽  
Hsi Color Model ◽  
Tracking Device

<p>Auto tracking mobile robot is a device that able to detect and track a target. For an auto tracking device, the most crucial part of the system is the object identification and tracking of the moving targets. In order to improve the accuracy of identification of object in different illumination and background conditions, the implementation of HSI color model is used in image processing algorithm. In this project HSI-based color filtering algorithm were used for object identification. This is because HSI parameter are more stable in different light and background conditions, so it is selected as the main parameters of this system. Pixy CMUcam5 is used as the vision sensor while Arduino Uno as the main microcontroller that controls all the input and output of the device. Besides that, L293D is used as the motor driver to control the movement of two DC motors that attached to the wheel of the robot. Moreover, two servo motors were used to control the pan-tilt movement of the vision sensor. Experimental results demonstrate that when HSI color-based filtering algorithm is applied to visual tracking it improves the accuracy and stability of tracking under the condition of varying brightness, or even in the low-light-level environment. Besides that, this algorithm also prevents tracking loss due to object color appears in the background.</p>

Chromatin Detection in Malaria Thick Blood Film Using Automated Image Processing

2015 ◽  
Vol 781 ◽  
pp. 616-619 ◽  
Author(s):  
Aeggarut Pinkaew ◽  
Tulaya Limpiti ◽  
Akraphon Trirat
Keyword(s):  
Image Processing ◽  
Blood Film ◽  
Color Model ◽  
Threshold Selection ◽  
Image Processing Algorithm ◽  
Thick Blood Film ◽  
Malaria Species ◽  
Hsi Color Model ◽  
Automated Image Processing ◽  
Global Health Problem

Malaria is a serious global health problem and rapid, accurate diagnosis is required to control the disease. An image processing algorithm to aid the diagnosis of malaria on thick blood films is developed. Morphological and automatic threshold selection techniques are applied on two color components from the HSI color model to identify chromatins of P. Falciparum and P. Vivax malaria species on the images. Chromatins are positively identified with good sensitivities for both species. After identifying the position of chromatins, the algorithm splits the image into small sub-images, each with a chromatin in the center. These small images can subsequently be used by technician to classify malaria species more conveniently.

scholarly journals Study on image processing method to classify objects on dynamic conveyor

2020 ◽  
Vol 2 (SI2) ◽  
pp. First
Author(s):  
Huy Ngoc Tran
Keyword(s):  
Image Processing ◽  
High Speed ◽  
Object Identification ◽  
Industrial Robots ◽  
Robotic Arm ◽  
Robot Arm ◽  
Vision Sensor ◽  
Image Processing Algorithm ◽  
Product Packaging ◽  
Object A

Controlling a robotic arms for applications such as detection and classification moving object using the vision sensor is a trend in the field of industrial robots. In particular, the vision sensor is the "eye" of the robot. To solve this problem, we need an efficient image processing algorithm for object identification to optimize the speed. Our classification principle based on the color of the object to be classified first, then separating contour to classify according to the shape of the object. In addition, our paper also propose a classification method that rarely mentioned in the relevant documents that classify based on object's characteristic. In fact, the product packaging not only has one color, but also includes complex color and patterns. Being able to classify these products shows the practicality of the proposed method. For complex colors and patterns object, the PCASIFT algorithm is useful, where SIFT extracts the local characteristics of the object and PCA reduces the number of dimensionality and retain only the best characteristics for identification. To picking object, a proposed design with the optimal requirements of picking order so that picking time is the shortest to minimize the delay for the next picking. The other outstanding advantage is a system of robotic arm to perform pick-up and sorting. This helps to verify good running algorithms in real time. The items are randomly released and the rotation of items is random. The speed of the conveyor is 5cm/s, an average of more than 2 seconds to pick up an object and robot arm processing precisely at high speed. The experimental results using camera Logitech C270, Yamaha Scara YK-400X robotic arm, LabVolt conveyor and OpenCV library are satisfactory, reliable and applicable.

Fuzzy Logic Controller for Obstacle Avoidance of Mobile Robot

2019 ◽  
Vol 20 (1) ◽  
pp. 51-62
Author(s):  
Rajmeet Singh ◽  
Tarun Kumar Bera
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Dynamic Model ◽  
Obstacle Avoidance ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Bond Graph ◽  
Membership Functions ◽  
Current Position ◽  
Dc Motors

AbstractThis work describes design and implementation of a navigation and obstacle avoidance controller using fuzzy logic for four-wheel mobile robot. The main contribution of this paper can be summarized in the fact that single fuzzy logic controller can be used for navigation as well as obstacle avoidance (static, dynamic and both) for dynamic model of four-wheel mobile robot. The bond graph is used to develop the dynamic model of mobile robot and then it is converted into SIMULINK block by using ‘S-function’ directly from SYMBOLS Shakti bond graph software library. The four-wheel mobile robot used in this work is equipped with DC motors, three ultrasonic sensors to measure the distance from the obstacles and optical encoders to provide the current position and speed. The three input membership functions (distance from target, angle and distance from obstacles) and two output membership functions (left wheel voltage and right wheel voltage) are considered in fuzzy logic controller. One hundred and sixty-two sets of rules are considered for motion control of the mobile robot. The different case studies are considered and are simulated using MATLAB-SIMULINK software platform to evaluate the performance of the controller. Simulation results show the performances of the navigation and obstacle avoidance fuzzy controller in terms of minimum travelled path for various cases.

scholarly journals A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3583 ◽  
Author(s):  
Shiping Ma ◽  
Hongqiang Ma ◽  
Yuelei Xu ◽  
Shuai Li ◽  
Chao Lv ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Color Space ◽  
Color Model ◽  
Low Light ◽  
Image Brightness ◽  
Low Contrast ◽  
Light Sensor ◽  
Hsi Color Model ◽  
Art Research ◽  
Enhancement Algorithm

Images captured by sensors in unpleasant environment like low illumination condition are usually degraded, which means low visibility, low brightness, and low contrast. In order to improve this kind of images, in this paper, a low-light sensor image enhancement algorithm based on HSI color model is proposed. At first, we propose a dataset generation method based on the Retinex model to overcome the shortage of sample data. Then, the original low-light image is transformed from RGB to HSI color space. The segmentation exponential method is used to process the saturation (S) and the specially designed Deep Convolutional Neural Network is applied to enhance the intensity component (I). At the end, we back into the original RGB space to get the final improved image. Experimental results show that the proposed algorithm not only enhances the image brightness and contrast significantly, but also avoids color distortion and over-enhancement in comparison with some other state-of-the-art research papers. So, it effectively improves the quality of sensor images.

scholarly journals Mean-Based Breakpoint Selection on Circular Histogram

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jiulun Fan ◽  
Jipeng Yang
Keyword(s):  
Selection Criteria ◽  
Color Image ◽  
Selection Criterion ◽  
Threshold Value ◽  
Simple Expression ◽  
Circular Data ◽  
Cumulative Distribution ◽  
Color Model ◽  
Hsi Color Model ◽  
Selection Of

Circular histogram represents the statistical distribution of circular data; the H component histogram of HSI color model is a typical example of the circular histogram. When using H component to segment color image, a feasible way is to transform the circular histogram into a linear histogram, and then, the mature gray image thresholding methods are used on the linear histogram to select the threshold value. Thus, the reasonable selection of the breakpoint on circular histogram to linearize the circular histogram is the key. In this paper, based on the angles mean on circular histogram and the line mean on linear histogram, a simple breakpoint selection criterion is proposed, and the suitable range of this method is analyzed. Compared with the existing breakpoint selection criteria based on Lorenz curve and cumulative distribution entropy, the proposed method has the advantages of simple expression and less calculation and does not depend on the direction of rotation.

Binocular Vision Sensor (Kinect)-Based Pedestrian Following Mobile Robot

2014 ◽  
Vol 670-671 ◽  
pp. 1326-1329 ◽  
Author(s):  
Huang Zhang ◽  
Rui Jun Yan ◽  
Wen Shen Zhou ◽  
Long Sheng
Keyword(s):  
Mobile Robot ◽  
Binocular Vision ◽  
Three Dimensional ◽  
Experimental Result ◽  
Vision Sensor ◽  
Human Skeleton ◽  
Depth Cameras ◽  
Left Middle

This paper present a pedestrian following mobile robot with binocular vision sensor. Because Kinect is one of the most inexpensive devices of depth-cameras, it is used in our application. Human skeleton is extracted by using Kinect, and the location of human is checked by projecting the three-dimensional (3D) pose of skeleton onto 2D screen. This 2D screen is separated into three parts, left, middle and right. Mobile robot rotates and translates according to the corresponding location of pedestrian. To make the robot move forward and backward, the distance between spine point and mobile robot is calculated. Finally, a real experimental result is used to validate our proposed method.

Application of Mobile Robots to Linear Control Theory Education

2020 ◽  
Vol 1 (3) ◽  
Author(s):  
Satoko Yamakawa
Keyword(s):  
Control Theory ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Control Systems ◽  
Linearization Method ◽  
Linear Control ◽  
Linear Control Systems ◽  
Control Engineering ◽  
Linear Control Theory ◽  
Dc Motors

Abstract The knowledge of control engineering for mechanical engineers seems to become more important with the continuous development of automated technologies. To cultivate this knowledge, many experimental devices have been proposed and used. Devices with direct current (DC) motors are widely used because the DC motors can be controlled with sufficient accuracy based on the classical linear control theory. Mobile robots are used as educational platforms attracting the attention of students in various problem-based learning subjects. However, they have been hardly used to teach linear control theory because of the nonlinearity. This paper shows an experimental curriculum to learn control theory using a mobile robot instead of a motor. Although the model of the mobile robot is nonlinear, a strict linearization method makes it possible to adjust the control gains using the linear control theory. By applying the method, the characteristics of linear control systems are explicitly observed in the traveling paths of the mobile robot, so an experimental curriculum to learn the basic linear control theory can be realized using an inexpensive mobile robot. The proposed experimental curriculum was carried out in a class of a mechanical engineering course, and its results are discussed in this paper.

Sign in / Sign up

Export Citation Format

Share Document