DEVELOPMENT AND MODELING OF UNMANNED UNDERWATER REMOTELY OPERATED VEHICLE USING SYSTEM IDENTIFICATION FOR DEPTH CONTROL

This paper presents the development and modeling of low cost underwater Remotely Operated Vehicle (ROV) for depth control using system identification technique. The ROV was developed by the Underwater Technology Research Group (UTeRG). For Unmanned Underwater Vehicle (UUV), the most crucial issue is the control system. It is needed for the ROV to perform several underwater applications and tasks. In this project, a prototype of the ROV will be developed first. The ROV will be tested on an open loop system to obtain measured input-output signals. Input and Output signals from the system are recorded and analyzed to infer a model. Then, system identification toolbox in MATLAB will be applied to generate a model of the ROV. The experimental testing of ROV only considered the vertical movement. The modeling obtained will be used to design the a suitable controller for depth control. The purpose of depth control is to ensure the ROV to remain stationary at a desired depth by utilizing the pressure sensor as feedback. The simulation studies have been carried out in order to obtain the controller of the ROV. This method is useful to obtain the model of the ROV to design the best controller for depth control. Conventional controller will be used in order to verify the modeling of ROV and gives acceptable performances of system response

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

Development of Subsea Altimeter Sensor System (SASS) Using Portable Sonar Sensor Fish Finder Alarm for Unmanned Underwater Vehicles

This paper describes the development of Subsea Altimeter Sensor System (SASS) for Unmanned Underwater Vehicles (UUV) Application using portable sonar sensor fish finder alarm system. Altimeter Sensor system is used to measure the depth of water. This altimeter sensor design valid for shallow water depth ranges maximum 100 m. This SASS will be applied to Underwater Remotely Operated Vehicles (ROV) design to verify the SASS performances. Experiments conducted to measure a depth of lab test, swimming pool test and Ayer Keroh Lake test. The experiments conducted in lab pool and swimming pool to measure and estimate the error and accuracy of SASS performances because of known the depth of water. The error of Altimeter Sensor System is 10% or ± 5 cm depth and accuracy of SASS very high about 90% for the both experiments. The results on Lake of Ayer Keroh at certain point can be acceptable. The 3D design of seabed mapping is plotted using MATLAB and Excel

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

Design and Development of Low Cost Certified Green Building for Non Residential Existing Building (NREB)

The Green Building Index (GBI) is one of rating tool which are provides a prospect for building developers and owners for designing and constructing a green and sustainable buildings. The proposed low cost GBI buildings provide many advantages such as energy savings, water savings, a healthier indoor environment, and better connectivity to public transport. Besides, adoption of recycling and greenery for the projects and can reduce the impact on the environment. However, the implementation to certify as Green Building Index has a lot of concerns such as cost constraint, know how constraints and etc. Therefore, in this paper, the design and development of low cost certified green building by fulfilling the Green Building Index (GBI) is proposed in order to ease the development of green building to have better life for human and environment in this world in term of energy efficiency performances

Neural Network Predictive Control (NNPC) of a Deep Submergence Rescue Vehicle (DSRV)

In this paper, the modeling and design of the depth control systems using Neural Network Predictive Control (NNPC)for a small unmanned underwater vehicle (UUV) will be described. Underwater vehicles consist of robotic vehicles that have been developed to reduce the risks of human life and to carry out tasks that would be impractical with a manned mission. The design of a depth control of an UUV is described in this paper. The main purpose of the underwater vehicle is that the vehicle must be stable over the entire range of operation. These techniques have the purpose of ensuring zero steady state error and minimum error in response to step commands in the desired depth.The depth performance for NNPC is discussed in terms of error and execution time. This NNPC will be compared with conventional controller such as PD controller and also by using the Fuzzy Logic Controller (FLC). For the comparison of computational time between this controllers, it can be observed that Fuzzy Logic is faster and neural network predictive controller is the slowest between them. It has been shown that the neural network predictive controller improved the transient response and error measure which shows the effectiveness of the designed controller

Dynamic Mathematical Modeling and Simulation Study of Small Scale Autonomous Hovercraft

Nowadays, various mechanical, electrical systems or combination of both systems are used tohelp or ease human beings either during the daily life activity or during the worst condition faced by them. The system that can be used to increase human life quality are such as in military operations, pipeline survey, agricultural operations and border patrol. The worst condition that normally faced by human are such as earthquake, flood, nuclear reactors explosion and etc. One of the combinations of both systems is unmanned hovercraft system which is still not thoroughly explored and designed. Hovercraft is a machine that can move on the land surface or water and it is supported by cushion that has high compressed air inside. The cushion is a close canvas and better known as a skirt. A hovercraft moves on most of surfaces either in rough, soft or slippery condition will be developed. The main idea for this project is to develop a dynamic modelling and controller for autonomous hovercraft. The model of the hovercraft will be initially calculated using Euler Lagrange method. The model of the hovercraft is derived using Maple software. The model that is developed then needs to be tested with open loop simulation in the MATLAB/Simulink environment. The LQR controller to regulate the small scale autonomous hovercraft then will be developed and tested with MATLAB

Modelling and Analysis of All Terrain Vehicle (ATV) using System Identification for Yaw Stability

This paper presents the modelling and analysis of path-following planning motion of an All-Terrain Vehicle (ATV) using system identification technique in term of yaw stability. The modelling is based on the single track and established by using Newtonian equation motion. Mathematical modelling is constructed in form of state space equation with the parameters used are measured through physical measurement of prototype ATV. Based on this model selection, the open loop system is simulated and the result will be validated by using system identification. Inertial Measurement Unit (IMU) sensor is used to collect and measure the data for the path-following planning. The analysis results for yaw stability of prototype ATV are validated by system identification method with step response approach. Both of the simulated and measured data is compared and the data is estimated to get the best fit for yaw estimation by using complimentary filter technique. From the result, the best fit for yaw estimation is 91.96% and considered as stabilized at steering angle 450