OPTIMAL CONTROL OF AUTONOMOUS UNDERWATER VEHICLE (AUV)

USING GENETIC ALGORITHMS

LEE HUEY YEE

This report is submitted in partial fulfilment of the requirements for the award of

Bachelor of Electronic Engineering (Computer Engineering) With Honours

Faculty of Electronic and Computer Engineering

Universiti Teknikal Malaysia Melaka

April 2010

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UNIVERSTI TEKNIKAL MALAYSIA MELAKAFAKULTI KEJURUTERAAN ELEKTRONIK DAN KEJURUTERAAN KOMPUTER

BORANG PENGESAHAN STATUS LAPORAN

PROJEK SARJANA MUDA II

Tajuk Projek : OPTIMAL CONTROL OF AUTONOMOUS UNDERWATER VEHICLEUSING GENETIC ALGORITHMS

Sesi Pengajian : 2009/2010

Saya Lee Huey Yeemengaku membenarkan Laporan Projek Sarjana Muda ini disimpan di Perpustakaan dengan syarat-syarat kegunaan seperti berikut:1. Laporan adalah hakmilik Universiti Teknikal Malaysia Melaka.

2. Perpustakaan dibenarkan membuat salinan untuk tujuan pengajian sahaja.

3. Perpustakaan dibenarkan membuat salinan laporan ini sebagai bahan pertukaran antara institusi

pengajian tinggi.

4. Sila tandakan ( √ ) :

SULIT*

(Mengandungi maklumat yang berdarjah keselamatan ataukepentingan Malaysia seperti yang termaktub di dalam AKTARAHSIA RASMI 1972)

TERHAD*(Mengandungi maklumat terhad yang telah ditentukan olehorganisasi/badan di mana penyelidikan dijalankan)

TIDAK TERHAD

Disahkan oleh:

__________________________ ___________________________________

(TANDATANGAN PENULIS) (COP DAN TANDATANGAN PENYELIA)

Alamat Tetap:

17-11, TAMAN PERMATA, JALAN HAJI JAIB,84000, MUAR, JOHOR

Tarikh: ……………………….. Tarikh: ………………………..

“I hereby declare that this report is the result of my own work except for quotes as

cited in the references”

Signature : ………………….

Author : Lee Huey Yee

Date : ………………….

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“I hereby declare that I have read this report and in my opinion this report is

sufficient in terms of scope and quality for the award of Bachelor of Electronic

Engineering (Computer Engineering) with Honours”

Signature : …………………………..

Supervisor’s Name : Madam Wong Yan Chiew

Date : …………………………..

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Special dedicated to my beloved parents, family, lecturers and fellow friends, who

had strongly encouraged, help and supported me in my entire journey of learning.

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ACKNOWLEDGEMENT

Firstly, thanks to my family who are always supportive upon assisting me in

completing my final year project and the thesis. Thanks for their understanding and

endless love through the duration of my studies.

Secondly, I am heartily thankful to my supervisor, Madam Wong Yan Chiew,

who was abundantly helpful and offered invaluable assistance support and guidance.

Also thanks for her encouragement, and support from the initial to the final level

enabled me to completing my final year project and my thesis.

Next, deepest gratitude and appreciations also to Mr. David Yap and Mr.

Sani Irwan Bin Md. Salim who are always helpful when needed. Without their

knowledge and assistance, this project would not have been successful.

Lastly, special thanks also to all my graduate friends, especially group

member Tan Chong Kai and Tam Wei Ee for sharing the literature and invaluable

assistance. I offer my regards and blessings to all of those who supported me in any

respect during the completion of the project.

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ABSTRACT

This thesis describes the optimal control of autonomous underwater vehicle

(AUV) with Genetic Algorithms (GA) Optimization. Due to the harsh and unstable

condition of underwater environment, the demand of AUV in underwater exploration

field is increasing rapidly.

AUV use in this project is low cost, small and light. Thus, it is more unstable

compare to other huge sized AUV. Its stability is easily affected by several factors,

such as underwater wave current and other unpredicted underwater condition. As a

result, the process of capturing data is more difficult and the quality of data obtained

is low and inaccurate.

. Objective in this project is to overcome the current weaknesses, by

implementing GA in Matlab environment for stability control and obstacle avoidance

purpose. Genetic algorithm is a search technique used in computing to find exact or

approximate solutions to optimization and search problems. Besides, fitness

functions are developed in order to optimize the movement of AUV.

Initially, analysis of the fitness function developed is done by using some data

create manually. Data generated from the sensors will be fed to GA and applied it

into fitness functions. The best fitness value will be fed back to AUV in order to

control the motors propulsion force.

By implementing GA, AUV able to maintain its stability, avoid obstacles and

also travels at the certain distance from the seabed. The fitness functions of the

stability problem and simulation results are presented in this thesis.

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ABSTRAK

Tesis ini membahaskan kawalan optimum kenderaan bawah air autonomi

(AUV) dengan Algoritma Genetik (GA) Optimasi. Oleh kerana keadaan yang keras

dan tidak stabil di persekitaran bawah air, permintaan AUV dalam bidang eksplorasi

bawah air meningkat dengan cepat.

Disebabkan penggunaan AUV dalam projek ini adalah kos rendah, kecil dan

ringan. Jadi, perbandingan dengan AUV lain yang besar, kestabilannya adalah

mudah dipengaruhi oleh beberapa faktor, seperti gelombang air dan keadaan bawah

air lain yang tidak dijangkakan. Akibatnya, proses penggambilan data adalah lebih

sukar dan juga tidak tepat.

Objektif projek ini adalah untuk mengatasi kelemahan AUV dengan

menerapkan GA di lingkungan Matlab untuk pengendalian kestabilan dan tujuan

mengelakkan rintangan. Algoritma genetik adalah teknik carian yang digunakan

dalam komputasi untuk mencari atau anggaran penyelesaian yang tepat untuk

optimasi dan masalah carian. Selain itu, fungsi kecergasan dikembangkan untuk

mengoptimumkan pergerakan AUV.

Pada awalnya, analisis fungsi kecergasan yang diujikan dengan

menggunakan beberapa data yang dibuat secara manual. Data dihasilkan dari sensor

akan diberi kepada GA dan diterapkan ke dalam fungsi kecergasan. Nilai fitness

terbaik akan dikembalikan kepada AUV untuk mengawal kekuatan penggerak

motor.

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Dengan menerapkan GA, AUV mampu menjaga kestabilan, mengelakkan

rintangan dan juga bergerak pada jarak tertentu dari dasar laut. Fungsi kecergasan

dan hasil simulasi dibincangkan dalam tesis ini.

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TABLE OF CONTENTS

CHAPTER TITLE PAGES

PROJECT TITLE i

DECLARATION OF THE PROJECT STATUS ii

DECLARATION iii

SUPERVISOR APPROVAL iv

DEDICATION v

ACKNOWLEDGEMENT vi

ABSTRACT vii

ABSTRAK viii

TABLE OF CONTENTS x

LIST OF TABLE xiv

LIST OF FIGURE xv

LIST OF ABBREVIATION xvii

LIST OF APPENDIX xviii

I INTRODUCTION

1.0 Introduction 1

1.1 Background 1

1.2 Problem Statement 2

1.3 Objective 3

1.4 Scope 4

1.5 Important of Project 4

1.6 Report Layout 5

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II LITERATURE REVIEW

2.0 Introduction 6

2.1 Autonomous Underwater Vehicle 7

2.2 Genetic Algorithm 8

2.2.1 Genetic Algorithm Overview 9

2.2.2 Search Space 9

2.2.3 Selection Stage 11

2.2.4 Crossover Stage 12

2.2.5 Mutation Stage 14

2.2.6 Differences between Genetic Algorithm and

Traditional Methods

16

2.2.7 Comparison with Other Methods 16

2.2.7.1 Neural Network 17

2.2.7.2 Random Search 17

2.2.7.3 Gradient Methods 17

2.2.7.4 Iterated Search 18

2.3 Matlab 18

2.3.1 The Matlab GA Toolbox 19

2.3.2 Data Structures 20

2.3.3 Toolbox Structure 20

2.3.3.1 Population Initialization 20

2.3.3.2 Fitness Assignment 22

2.3.3.3 Selection Functions 23

2.3.3.4 Reproduction Operators 24

2.3.3.5 Mutation Operators 25

2.3.3.6 Crossover Operators 26

2.3.3.7 Migration Options 27

2.3.3.8 Algorithm Settings 28

2.3.3.9 Hybrid Function Options 28

2.3.3.10 Stopping Criteria Options 29

2.3.3.11 Plot Options 30

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2.3.3.12 Output Function Options 31

2.3.3.13 Display to Command Window Options 32

2.3.3.14 User Function Evaluation 33

2.4 Summary 33

III METHODOLOGY

3.0 Introduction 34

3.1 Flow Chart Diagram 34

3.2 Block Diagram of the Project 36

3.3 Summary 39

RESULT

4.0 Results 40

4.1 Structure of AUV 41

4.2 Results Based On Accelerometer 42

4.3 Results Based On Sonar Sensors 50

4.4 Results Based On Accelerometer And Sonar Sensors 58

V VALIDATION

5.0 Introduction 60

5.1 Experiment 1: Maintain A Constant Distances From

The Surface

61

5.2 Experiment 2: Obstacles Avoidance 66

VI CONCLUSION 70

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REFERENCES 72

APPENDIX 74

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LIST OF TABLE

NO. TITLE PAGES

4.1 Position of AUV 44

5.1 Parameters of the experiments 61

5.2 Fitness values of GA based on sensors data 62

5.3 Overall movement of AUV 63

5.4 Speed of motor diverse from constant speed 64

5.5 Fitness values of GA based on sensors data 67

5.6 Overall movement of AUV 67

5.7 Angle of AUV to be torque 68

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LIST OF FIGURE

NO. TITLE PAGES

2.1 Example of Roulette Wheel 11

2.2 Example of Genetic Crossover 13

2.3 Example of Genetic Mutation 14

2.4 Genetic Algorithm Architecture 15

2.5 GA Tool Box 19

2.6 Population Option 21

2.7 Fitness Scaling Options 22

2.8 Selection Options 23

2.9 Reproduction Options 24

2.10 Mutation Function Options 25

2.11 Crossover Function Options 26

2.12 Migration Options 27

2.13 Algorithm Settings 28

2.14 Hybrid Function Options 29

2.15 Stopping Criteria Options 30

2.16 Plot Options 31

2.17 Output Function Options 32

2.18 Display To Command Window Options 32

2.19 User Function Evaluation Options 33

3.1 Flow Chart Diagram 35

3.2 Overview of Connection Between AUV And GA 36

3.3 Example of .Mat File 37

3.4 Options of Parameter 37

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3.5 Constraints 38

3.6 Fitness Function And Number of Variable Column 38

4.1 Prototype of AUV 41

4.2 Data of Sensors Shown In .Mat File of MATLAB 42

4.3 Axes Plane of AUV 43

4.4 Position of AUV 43

4.5 Motor Movements of AUV Based On X Axis 47

4.6 Motor Movements of AUV Based On Y Axis 48

4.7 Motor Movements of AUV Based On Z Axis 49

4.8 Final Movements of Motors Based On Accelerometer 50

4.9 Motor Movements of AUV Based On Left Sonar 53

4.10 Motor Movements of AUV Based On Right Sonar 54

4.11 Motor Movements of AUV To Maintain The Constant Depth 56

4.12 Final Movements of Motors Based On Sonar Sensors 57

4.13 Final Movements of Motors Based On Combination of

Accelerometer And Sonar Sensors

59

5.1 Randomly Generated Landscape 61

5.2 Path of AUV 64

5.3 Path of AUV After Optimized By GA 65

5.4 Randomly Generated Obstacles 66

5.5 Path of AUV 68

5.6 Path of AUV After Optimized By GA 69

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LIST OF ABBREVIATION

AUV - Autonomous Underwater Vehicle

GA - Genetic Algorithms

MATLAB - Matrix Laboratory

DNA - Deoxyribonucleic acid

CACSD - Computer Aided Control System Design

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LIST OF APPENDIX

NO. TITLE PAGES

A Get Data File 75

B Main File 76

C Accelerometer Main File 77

D Accelerometer Axis File 79

E Accelerometer Fitness Function File 80

F Sonar Sensor Main File 81

G Sonar Sensor Movement File 83

H Sonar Sensor Fitness Function File 84

I Journal Under Review – Development of Genetic Algorithms In

Autonomous Underwater Vehicle

85

J Journal Under Review – Orientation Control of AUV Using

Genetic Algorithms

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K Optimal Control Of Autonomous Underwater Vehicle By Genetic

Algorithms

108

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CHAPTER I

INTRODUCTION

1.0 Introduction

Chapter one is discuss about the background, problems statement, objectives,

scope and important of the projects. For background, the basic idea of Autonomous

Underwater Vehicle (AUV) and Genetic Algorithm (GA) is introduced. Next, the

problem statements faced in this project is clarified. Furthermore, based on the

problem statement, the objectives and scope of the project is set and indentified.

Lastly, the important of this project is discussed.

1.1 Background

Autonomous Underwater Vehicle (AUV) is a type of underwater robotic

device which can drive through the underwater propulsion system without any

human controls. It is self-piloted where it is using the feedback received from the

surrounding in order to determine its actions and movement during operation.

Genetic Algorithms (GA) are adaptive methods which may be used to solve

search and optimization problems. They are based on the genetic processes of

biological organisms. Over many generations, natural populations evolve according

to the principles of natural selection and survival of the fittest. GA can quickly scan a

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vast solution set. Bad proposals do not affect the end solution negatively as they are

simply discarded. The inductive nature of the GA means that it doesn't have to know

any rules of the problem - it works by its own internal rules. This is very useful for

complex or loosely defined problems.

1.2 Problem Statement

The Autonomous Underwater Vehicle (AUV) in this project is low cost,

small and light; it is more unstable compare to other huge sized AUV. Since it is a

light-weight type AUV, its stability is easily affected by the propagation of wave

underwater and the force of wave will make it out of course. Furthermore, its

stability is also easily affected by several factors where they are unpredicted such as

sea creatures and earthquake. As a result, the process of capturing data is more

difficult and the quality of data obtained is low and inaccurate. Therefore, GA which

is an adaptive method needed to be study and applied in the Matlab environment to

solve the search and optimization problems.

Besides, sensitivity of AUV can be increased by increasing number of

sensors; therefore, there is a lot of sensor data needs to analyze and optimize on real

time simulation. These sensors need to process in order to produce corresponding

output and subsequently control the movement of AUV. As a result, fitness functions

of GA are needed to develop and apply in AUV. Subsequently, the best fitness value

obtained from the fitness functions will be used to optimize the movement of AUV.

In current technology, there is lack of AI or GA implementation into AUV

purpose. Generally, GA is commonly used in path-finding which it is used in finding

the closet and shortest path between two places by given many different routes.

Therefore, GA is only used in particular field as it is not a very common algorithm to

people.

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1.3 Objectives

In this project, there are four objectives are stated for the achievement of the

project.

i. To study Genetic Algorithm

ii. To develop Genetic Algorithm in Matlab environment

iii. To apply fitness function in AUV, in order to optimize its movement

iv. To control the stability of AUV by analyzing data from sensors

The main objective of this project is to study Genetic Algorithm (GA) in

order to apply in AUV. Therefore, basic model and elements of GA will be

understood and analyzed. Basically, GA contains of three main stages, which are

selection, crossover, and mutation.

Secondly, Matlab environment is used in order to develop the GA. Using

Matlab, it has the toolbox that support GA and very suitable for beginner to study the

process and flow of GA.

Thirdly, the fitness function of GA is required to apply in AUV. The

development of fitness function for stability optimization is first undergoes and the

final best fitness value is integrated into the AUV.

Finally, the data fed in by sensors used is analyzed in order to control the

stability of AUV. The sensors first get data from surrounding as parameter to

determine its actions and movements later by using GA.

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1.4 Scope

The scope of this project is using GA as the controller of AUV in order to

control AUV’s stability. Moreover, the implementation of GA is developed in

Matlab environment with the aid of GA Tool as fundamental tool to further

understand the characteristic of GA.

Furthermore, optimization of a group of sensors data that feedback from

AUV is undergoes through GA analysis in Matlab. After the analysis, the result will

be perform as the optimized output to control the movement and position of AUV

back to its stable state.

Besides that, the development of the fitness function for stability

optimization and obstacles avoidance is carry out using Matlab. Eventually, the best

fitness function will be used and implemented in GA for stability control and

obstacle avoidance for AUV.

1.5 Important of Project

The important of project is the implementation of GA in Matlab environment.

By using the GA concept, AUV always can be optimized in balance position and

will not crash onto obstacles. Through GA, variety of sensors data can be analyzed

and optimized in real time simulation.

This project also contributes to provide the solution to the stability control

and obstacles avoidances problem by implementing GA in AUV. Sensors data from

AUV will always being optimized and feedback to AUV for further processes.

Besides, through this project, the AUV can react autonomously by itself

which it will depend on the data feedback from the sensors. As the outcome of this

project, the AUV designed will be fully autonomous without the needs of remote

control.

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1.6 Report Layout

This thesis consists of six main chapters where all the chapters are essential

to describe all the architecture and functionality of the project itself. Chapter I

describes briefly about the project’s introduction. It is also discuss about the

objectives, scopes of project and project application.

Chapter II describes about the literature review that consists of the

background of the project. It also describes about the same projects that have been

done of previous person and it is more to make research on different, advantages and

disadvantages of previous project.

Chapter III describes about the methodology of this project. It defines the

physical block diagram and flow chart operation of GA. Chapter IV describes

matters regarding results and analysis on the subjected items. Chapter V will discuss

about the experimental results and the validation part. Lastly, Chapter VI is describes

about conclusion and recommendation. This section will conclude about knowledge

that comes out from the project and some recommendation to the further study

regarding to the GA.

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CHAPTER II

LITERATURE REVIEW

2.0 Introduction

This chapter is discussed about the literature review that consists of the

background of the project, which includes Autonomous Underwater Vehicle (AUV),

Genetic Algorithms (GA), Matlab and the comparison between the several

algorithms are made.