Local threshold identification and gray level classification of butt joint welding imperfections using robot vision system

This research is carried out be able to automatically identify the joint position and classify the quality level of imperfections for butt welding joint based on background subtraction, local thresholding and gray level approaches without any prior knowledge of the joint shapes. The background subtraction and local thresholding approaches consist of image pre-processing, noise reduction and butt welding representation algorithms. The approaches can automatically recognize and locate the butt joint position of the starting, middle, auxiliary and ending point according to the three different joint shapes; straight line, tooth saw and curved joint shapes. The welding process was done by implemented an automatic coordinate conversion between camera (pixels) and KUKA welding robot coordinate (millimeters) from the KUKA welding robot and camera coordinate ratio. The ratio was determined by a camera and three reference point (origin, x-direction and y-direction) taken around workpiece. Hence, the quality level of imperfection for butt welding joint was classified using Gaussian Mix Model (GMM), Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM) classifiers according to their class of imperfection categories; good welds, excess welds, insufficient welds and no weld in each welding joint shape. These classifiers introduced 72 characteristics of feature values of gray pixels taken from co-occurrence matrix. The feature values consist of energy, correlation, homogeneity and contrast combine with gray absolute histogram of edge amplitude including additional characteristic features with scaled image factor by 0.5. The proposed approaches were validated through experiments with a KUKA welding robot in a realistic workshop environment. The results show that the approaches introduced in this research can detect, identify, recognize, locate the welding position and classify the quality level of imperfections for butt welding joint automatically without any prior knowledge of the joint shapes

Develop and Implementation of Autonomous Vision Based Mobile Robot Following Human

This project related to develop and implementation of autonomous vision based mobile robot following human. Human tracking algorithm will be developed to allow a mobile robot to follow a human

Shape-Based Matching: Application of Defect Detection

This research regrading to the application of a vision algorithm sensor to monitor the operation of a system in order to control the concerning jobs and work pieces recognition that are to be made during system operation in real time

ROBUST CONTROL OF ADAPTIVE SINGLE INPUT FUZZY LOGIC CONTROLLER FOR UNMANNED UNDERWATER VEHICLE

In this paper the investigation of Adaptive Single Input Fuzzy Logic Controller (ASIFLC) as robust control of an Unmanned Underwater Vehicle (UUV). Robust control methods are designed to function properly with a present of uncertain parameters or disturbances. Robust control methods aim to achieve robust performance and stability in the presence of bounded modeling errors. The UUV applied in this research is a Remotely Operated Vehicle (ROV). Three ROV model will be used to apply ASIFLC such as ROV model was developed by UTeRG Group, ROV Model “Mako” was developed by Louis Andrew Gonzalez and RRC ROV- unperturbed with 6 DOF was developed by C.S. Chin. The simulation of controlling ROV by ASIFLC focused on depth control (heave motion). The ASIFLC for depth control of the ROV was successfully tested in simulation and real time by UTeRG Group. The simulation uses MATLAB Simulink and the performances of system response for depth control of Adaptive Single Input Fuzzy Logic Controller for Unmanned Underwater Vehicle will be discussed. It is proved the Adaptive Single Input Fuzzy Logic Controller is the robust control for different model of the ROV

AUTONOMOUS MOBILE ROBOT VISION BASED SYSTEM: HUMAN DETECTION BY COLOR

This project related to develop and implementation of autonomous vision based mobile robot following human based on clothes color. There have two part are involve which is mobile robot platform and classification algorithms by color. The core of the classification of color are comprise into two process; offline and online. An offline process consists of the training of the static image, using deference input sources that depend on the application. An online process consists of the matching process and the result of the clothes color position. Then classification algorithm is applied to find the centroid of the human. This centroid is then compared with the center of the image to get the location of the human with respect to the camera, either at the left or right of the camera. If the human is not in the center of the camera view, then corrective measures is taken so that the human will be in the center of the camera view. Data for the centroid of human is shown through the Graphical User Interface (GUI). One of the unique advantages in this project, the detection of human by color only uses image processing that generated by the algorithms itself without additional sensor like sonar or IF sensor

Fuzzy Logic Approach for Mobile Robot in Intelligent Space

This project introduces the fuzzy logic approach for mobile robot in intelligent space. There are three major algorithms involved. They are known as object classification, object tracking and obstacle avoidance. The inputs are received from cameras which are mounted at a ceiling. The main idea of the object classification is to classify object into three categories depending upon their colors; the categories are mobile robot, destinations and obstacle position. These categories are represented by X symbol with different colors. This system is to teach and train the mobile robot proceeding to destination without hitting the obstacle. The mobile robot is autonomous; that means, it could be pursuing to the target position automatically without user guided. In this project, fuzzy logic is use to guide the mobile robot direction until it reaches the target position. This system is generates in real-time and suitable for indoor environment applications. One of the unique advantages of this project is that it only uses, there only used a camera and image processing generated by the algorithms itself without additional sensor such as sonar or IR sensor

A Review On SVC Control For Power System Stability With And Without Auxiliary Controller

Since the beginning of the last century, power system stability has been recognized as a vital problem in securing system operation. Power system instability has caused many major blackouts. This paper reviewed the previous technical works consisting of various methods of optimization in controlling power system stability. The techniques presented were compared to optimize the control variables for optimization of power system stability. Power system stability enhancement has been investigated widely in literature using different ways. This paper is focusing on SVC performance for enhancing power system stability either through SVC controlled itself or SVC controlled externally by other controllers. Static VAR compensators (SVCs) are used primarily in power system for voltage control as either an end in itself or a means of achieving other objectives, such as system stabilization. The analysis on performance of the previous work such as advantages and findings of a robust method approach in each technique was included in this paper

Implementation of Shape – Based Matching Vision System in Flexible Manufacturing System

This research is regarding the application of a vision algorithm to monitor the operations of a system in order to control the decision making concerning jobs and work pieces recognition that are to be made during system operation in real time. This paper stress on the vision algorithm used which mainly focus on the shape matching properties of the product. The main focus of this paper is on the development of an adaptive training phase of the vision system, which is the creation of a flex- ible Region of Interest capability that is able to adapt to various type of applications and purposes depending on the users’ requirements. Additionally, an independent stand-alone control scheme was used to enable this system to be used in various types of manufacturing configurations. The system was tested on a number of different images with various characteristics and properties to determine the reliability and accuracy of the system in respect to different conditions and combination of different training traits

Implementation of Shape – Based Matching Vision System in Flexible Manufacturing System

This research is regarding the application of a vision algorithm to monitor the operations of a system in order to control the decision making concerning jobs and work pieces recognition that are to be made during system operation in real time. This paper stress on the vision algorithm used which mainly focus on the shape matching properties of the product. The main focus of this paper is on the development of an adaptive training phase of the vision system, which is the creation of a flexible Region of Interest capability that is able to adapt to various type of applications and purposes depending on the users’ requirements. Additionally, an independent stand-alone control scheme was used to enable this system to be used in various types of manufacturing configurations. The system was tested on a number of different images with various characteristics and properties to determine the reliability and accuracy of the system in respect to different conditions and combination of different training traits

Fuzzy Controlled SVC For Power System Damping

This paper presents the ability of the fuzzy logic-based stabilizer used to generate the supplementary voltage control signal of the SVC to improve the damping of the inter-area mode oscillation in the power system. The base system is symmetrical, consisting of two identical areas connected by a relatively weak tie line. The SVC is chosen to be installed at the tie line midpoint. The active power of the local line will be used as an input signal for the stabilizer. The additional signal is calculated using fuzzy membership function to determine the quantity of reactive power supplied absorbed by SVC. The system oscillation is indicated by a 3-phase-to-ground short circuit occurring at 0.2s of the simulation and subsequently clearing after 100ms. Simulation with the sample power system shows that when subjected to a disturbance, fuzzy logic-based SVC stabilizer provides good damping in inter-area mode oscillation for the system. The effectiveness of the stabilizer applied with and without PSS will also be investigated