Anti Collision Mechanism in Vehicles

Anti Collision Mechanism in

Vehicles

Submitted by:

Naeem Iqbal 2010-EE-092

Zohair Ali Sulahri 2010-EE-096

Mujahid Majeed 2010-EE-101

Usman Ahmed 2010-EE-109

Supervised by: Prof. Dr. Noor Muhammad Sheikh

Department of Electrical Engineering

University of Engineering and Technology Lahore

Anti Collision Mechanism in

Vehicles

Submitted to the faculty of the Electrical Engineering Department

of the University of Engineering and Technology Lahore

in partial fulfillment of the requirements for the Degree of

Bachelor of Science

in

Electrical Engineering.

Internal Examiner External Examiner

DirectorUndergraduate Studies

Department of Electrical Engineering

University of Engineering and Technology Lahore

i

Declaration

We declare that the work contained in this thesis is our own, except where explicitly

stated otherwise. In addition this work has not been submitted to obtain another degree

or professional qualification.

2010-EE-092:

2010-EE-096:

2010-EE-101:

2010-EE-109:

Date:

ii

Acknowledgments

We would like to thank our parents and teachers for supporting us and especially Dr.

Noor Muhammad Sheikh for helping us throughout this project. . .

iii

Dedicated to our parents and teachers whose unconditional supporthas always been there for our studies.

iv

Contents

Acknowledgments iii

List of Figures vi

List of Tables vii

Abbreviations viii

Abstract ix

1 Introduction 1

1.1 Obstacle Detection Techniques . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1.1 IR Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1.2 Sonar Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.1.3 Laser Scanning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.1.4 Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 Motivations and Problem Statement 4

2.1 Motivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.1.1 Risky Jobs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.1.2 Labor Shortage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.1.3 Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

3 Proposed Approach 6

4 Hardware Implementation and Design 8

4.1 Raspberry Pi Board . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

4.1.1 Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

4.1.2 Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

4.1.2.1 Operating System . . . . . . . . . . . . . . . . . . . . . . 9

4.1.2.2 Camera . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

4.1.2.3 GPIO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

4.1.2.4 Display . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

4.1.2.5 PWM Generation . . . . . . . . . . . . . . . . . . . . . . 10

4.2 Camera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

4.3 Atmega16L . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

4.4 Motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4.5 Motor Driver (H-Bidge) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

v

Contents vi

4.5.1 Direction Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4.5.2 Speed Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

4.6 Mechanical Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

5 Software Implementation 16

5.1 Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

5.2 Implementation Using Simulink . . . . . . . . . . . . . . . . . . . . . . . . 17

5.3 Block Diagrams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

6 Conclusion and Future Directions 21

A Circuit Diagram 22

A.1 PWM Generation Circuit . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

A.2 H-Bridge Circuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

B Codes 24

B.1 Code for PWM Generation in ATMEGA16L . . . . . . . . . . . . . . . . 24

B.2 Code for ACM in Raspberry Pi . . . . . . . . . . . . . . . . . . . . . . . . 24

References 28

List of Figures

1.1 Working of IR Sensor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Sonar Sensor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Laser Scanning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2.1 WHO Statistics About Road Accidents. . . . . . . . . . . . . . . . . . . . 4

3.1 Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

4.1 Raspberry Pi Board. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

4.2 Connections of Raspberry Pi and Atmega16L. . . . . . . . . . . . . . . . . 11

4.3 Logitech C210. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4.4 Typical H-Bridge. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

4.5 Direction Control of H-Bridge. . . . . . . . . . . . . . . . . . . . . . . . . 13

4.6 H-Bridge Motor Driver Hardware. . . . . . . . . . . . . . . . . . . . . . . 14

4.7 Hardware. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

5.1 Original and Edge Detected Image. . . . . . . . . . . . . . . . . . . . . . . 16

5.2 Edge Detection Using MATLAB. . . . . . . . . . . . . . . . . . . . . . . 17

5.3 Edge Detection Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . 18

5.4 Block Diagram of ACM in MATLAB. . . . . . . . . . . . . . . . . . . . . 18

5.5 Block Diagram of pyhton Algorithm. . . . . . . . . . . . . . . . . . . . . . 19

5.6 Division of input image. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

5.7 Canny Edge Detection Algorithm. . . . . . . . . . . . . . . . . . . . . . . 20

A.1 PWM Generator for H-Bridge. . . . . . . . . . . . . . . . . . . . . . . . . 22

A.2 H-Bridge Simplified Circuit. . . . . . . . . . . . . . . . . . . . . . . . . . . 23

A.3 H-Bridge Complete Circuit. . . . . . . . . . . . . . . . . . . . . . . . . . . 23

vii

List of Tables

4.1 Raspberry Pi Model B Specifications . . . . . . . . . . . . . . . . . . . . . 10

4.2 GPIO Truth Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

4.3 Motors Standard Parameters . . . . . . . . . . . . . . . . . . . . . . . . . 12

viii

Abbreviations

ACM Anti Collision Mechanism

PWM Pulse Width Modulation

USB Universal Serial Bus

RISC Reduced Instruction Set Computer

HDMI High Defination Multimedia Interface

UART Universal Asynchronous Transmitter

GPIO General Purpose Input Output

ix

Abstract

We have used image processing techniques to avoid obstacles in our prototype. These

images are taken from a camera mounted at the front of the vehicle. This stream of

images is fed to the Raspberry Pi to be processed , where Raspberry Pi makes the

prototype maneuver such that the obstacle can be avoided. This is a very basic form of

an autopilot system in vehicles.

Chapter 1

Introduction

Both humans and animals have different naturally built in system to avoid obstacles in

from of them. For example bats use echo vision to detect obstacles in front of them

without having any vision. Humans use their eyes to get a direct vision of the obstacle

which is processed by the brain to decide how to avoid the obstacle. Our prototype uses

the very same basic methodology where camera and processor act as a replacement of

eyes and brain. Based on the similarities with many living organisms there are many

different types of obstacle detection techniques which are described in 1.1

1.1 Obstacle Detection Techniques

Four famous obstacle detection techniques are:

IR(infra-red) Sensor

Sonar Sensor

Laser Scanning

Image Processing

1.1.1 IR Sensor

IR sensor technique uses an IR rays emitter and a receiver. It emits IR rays inter-

Figure 1.1: Working of IR Sensor.

mittently and from the difference between the sending and the receiving rays, obstacle

presence can be approximated. However, it is a very crude technique and has a very

1

Chapter 1. Introduction 2

limited range. Moreover, for the complete spanning of the front side, a whole array of IR

sensors would be required which would increase the complexity of the module. Figure

1.1 shows the working principle of these sensors.

1.1.2 Sonar Sensor

Sonar sensors emit ultrasonic waves and have a range of 0-255 inches. They also send

and receive rays to detect the obstacle. The problem with these sensors remains the

same that they have a limited range and give no idea about the size of the obstacle.

Moreover, the performance of the sonar sensor depends upon the environment conditions

too like surface condition and humidity etc. Sonar sensor is shown in figure 1.2

Figure 1.2: Sonar Sensor.

1.1.3 Laser Scanning

Laser scanning technique is better than the latter two techniques as it has a broad range

and can also determine the size of the obstacle. But for the sake of getting information

about the size of the obstacle, we have to install an array of laser beams and optical

sensors which would be both inefficient and expensive.

Figure 1.3: Laser Scanning.

1.1.4 Image Processing

In this technique, we can both detect the obstacle and also determine its size. There is

no concern about the range, because as long as the obstacle is in the video no matter

Chapter 1. Introduction 3

how small or how large, it will be detected. Moreover, it does not require any receiver.

Only a video camera would sufficient.

Analyzing the above mentioned techniques, Image processing turns out to be most effi-

cient. Hence in our project, we have implemented edge detection based image processing

algorithm to detect our obstacle. We will use less expensive and easily available resources

to lower the cost of this project.

Chapter 2

Motivations and Problem

Statement

Road accidents are one of the leading causes of human fatalities in most of the countries

around the globe. According to WHO 1.24 million died each year in road accidents

and more than 90% of these accidents are due to human error. Figure 2.1 shows some

statistics about the deaths in road accidents by WHO[1]. These accidents can be greatly

reduced if only there were an assistance system installed in the vehicle. Most of the

researchers are trying to produce such modules. We also aim to design a video camera

based system that avoids the obstacles in front of the vehicle automatically.

Figure 2.1: WHO Statistics About Road Accidents.

2.1 Motivations

There are many other factors that contribute to the development of automated vehicles.

Some of them are:

4

Chapter 2. Motivations & Problem Statement 5

Risky Jobs

Labor Shortage

Reliability

2.1.1 Risky Jobs

There are some jobs which involve high levels of risk and cannot be done without casu-

alties. For example in Coal Mining, large numbers of people die every year and many

others.

2.1.2 Labor Shortage

Many developed countries do not have the man force to drive their industry. Such an

unmanned vehicle can be a suitable alternative.

2.1.3 Reliability

Machines do not need to take rest and give a guaranteed efficiency round the clock.

2.2 Problem Statement

Considering the survey report, it is quite desirable to reduce the number of casualties

happening all over the world. These casualties can be reduced by adding an extra driver

assistant module installed in the vehicle. This module should work as an assistant to the

driver of the vehicle and if the driver somehow fails to avoid the obstacle, the module

takes over and avoids the collision of the vehicle; thus named as anti-collision module.

We have designed a prototype of such a module which uses a camera as a sensor of

obstacles, which is quite inexpensive considering the price of the vehicle.

Chapter 3

Proposed Approach

As the purpose of our project is to avoid collision by getting information about sur-

rounding environment. We are doing this with the help of image processing, which is

algorithmic technique and requires a central processing unit. We are using Raspberry

Pi board as central processing unit because it is small in size and also cheap as compare

to other boards and it fulfills our requirements. Raspberry Pi board is the main unit of

our project and takes all the decisions. It supports a large number of operating systems

as discuss in table 4.1, we are using Raspbian operating system. We are using web cams

instead of expensive cameras to capture images and send to Raspberry Pi board for pro-

cessing. All the components e.g Raspberry Pi, motor drivers, PWM generation circuits

and camera are places on mechanical structure as shown in figure 4.7. Figure 3.1 shows

the simple block diagram of our proposed approach. The camera captures the images

Figure 3.1: Approach.

and image processing algorithm runs in Raspberry Pi and divides the image into gray

scale apply CANNY EDGE Detector. Then after thresholding image is divided into two

parts i.e left and right. These images are converted into arrays using Numpy module

and then count the non zero entries in both the images. If the left and right image

has same number of zeros then Raspberry Pi gives interrupt signal to PWM generator

such that it varies the PWM in such a way that prototype moves forward, if left zeros

are greater than right zeros then prototype moves toward left and if left zeros are less

than right zeros then t moves toward right and this cycle continues. We have also set

a threshold value, below this value no interrupt signal is given to motor driver circuit

and prototypes continues its motion. We are generating four PWMs with the help of

Atmega16L micro controller, two for each motor. Speed can be adjusted by varying the

duty cycle of PWM. To prevent any damage to micro controller due to high voltages

6

Chapter 5. Proposed Approach 7

and current at motor end, PWM signal is given to motor driver (H-bridge) circuit with

the help of apto coupler TLP250. So if the anything goes wrong on motor side like short

circuit it cannot reach micro controller. In short an infinite loop is running in Raspberry

Pi which takes image as a input from web cam and reach to a secure decision and then

again takes image input and it continues.

Chapter 4

Hardware Implementation and

Design

Before going into design details lets have a brief introduction of all the components

which we used in this project. As we are using image processing for ACM so our

system consists of following components:

Raspberry Pi Board

Camera

Atmega16L Micro-controller

Motor

Motor Driver (H-Bridge)

Mechanical Structure

4.1 Raspberry Pi Board

Raspberry Pi is a credit card sized Single-board computer developed in UK by the

Raspberry Pi Foundation.

4.1.1 Hardware

Raspberry Pi is manufactured in two board configurations:

Model A

Model B

We have used Model B shown in figure 4.1 which has advantages of being available in our

universitys lab, Support for Camera, Two Usb Ports and an Ethernet 10/100 Controller

so that it can be used in Headless Mode. Table 4.1 shows the complete specifications of

Model B.

8

Chapter 3. Hardware Implementation and Design 9

Figure 4.1: Raspberry Pi Board.

4.1.2 Operation

4.1.2.1 Operating System

Raspberry Pi has many operating systems designed for it. We have used Raspbian

operating system. The reason for using Raspbian is that it is a LINUX integrated OS

and so has a LINUX GUI which we are already familiar with.

4.1.2.2 Camera

Raspberry Pi Foundation has manufactured a camera module for video capture opera-

tions but we have used a USB webcam to achieve our goal instead of using Raspi Cam,

because USB cam is much cheaper than Raspi Cam( Raspi Cam costs almost $40 along

with the shipment cost ). However, the results would have been far better if we had

used Raspi Camera module.

We have used Logitech C210 for our project. This camera is connecting using USB port

instead of CSI camera connector on the board. The RPI supported resolutions for this

camera are:

320 x 240

640 x 480


SoC Broadcom BCM2835 (CPU, GPU, DSP, SDRAM)

CPU 700 MHz ARM1176JZF-S core

GPU Broadcom VideoCore IV, OpenGL ES 2,1080p30 Full HD HP H.264

Memory 512 MB (shared with GPU)

USB 2 Ports 2

Video Input A CSI input connector for RPF designed camera module

Video Outputs Composite RCA, HDMI

Audio Outputs 3.5mm jack, HDMI

On Board Storage SD / MMC / SDIO card slot

On Board Network 10/100 Mbit/s Ethernet

Low-level Peripherals GPIO pins, SPI, I2C, UART

Power Ratings 700 mA (3.5 W)

Power Source 5 V via MicroUSB or GPIO header

Size 85.60 mm 56 mm

Operating System Arch Linux ARM, Debian GNU/Linux, Gentoo, Fedora,FreeBSD, NetBSD, Plan 9, Raspbian OS, RISC OS,Slackware Linux

Table 4.1: Raspberry Pi Model B Specifications

4.1.2.3 GPIO

We have used GPIO pins of the RPI to generate interrupts so that it can be received

by the micro controller and generated PWMs for the motors. The GPIO truth table for

the operations defined the Micro Controller is given in table 4.1.2.5.

BCM Mode RPI Pins/ Pin 17 Pin 18 Pin 4 FunctionATMEGA16 pins /Pin38 /Pin39 /Pin40

0 0 1 Forward

0 1 1 Turn Left

1 0 0 Turn Right

0 0 0 Stop

Table 4.2: GPIO Truth Table

4.1.2.4 Display

For working with our project instead of having an HDMI costly display device, we used

RPI in headless mode; with HEAD implying HDMI display device.

In headless mode, we connect our raspberry pi and laptop with the same LAN (local

area network) and open an SSH (Secure Shell) on our RPI using our laptop. Using SSH

approach, we have the window of the RPI on our laptop making it easy to work. One

of the many reasons why there is no windows operating system designed for RPI.

4.1.2.5 PWM Generation

RPI has a built-in support for generating PWM. However, we need 4 PWMs for the

proper working of two H-Bridge driven motors of the Robot, whereas RPI can generate


only one PWM. For that we have used ATMEGA16L to generate 4 PWMs which are

varied depending upon the input at the pin 38, 39 and 40 of the controller as shown in

figure 4.2. These inputs of the ATMEGA16L are driven by the GPIO output pins of

the RPI, as shown table .

Figure 4.2: Connections of Raspberry Pi and Atmega16L.

4.2 Camera

We are using Logitech C210 camera shown in figure 4.3. Specifications of this camera

are:

1.3 Mega Pixels Resolution

Auto Focus

Adjustable Base

USB 2 Interface

The purpose of this camera is to capture images and feed input to Raspberry Pi board

so that secure decision is made on the basis of algorithm designed.

4.3 Atmega16L

Atmega16L is a low-power CMOS 8-bit micro controller based on the AVR enhanced

RISC architecture. By executing powerful instructions in a single clock cycle, the AT-

mega16L achieves throughputs approaching 1 MIPS per MHz allowing to optimize power

consumption versus processing speed. Schematic is shown in Appendix ??

We generate for PWM at pin 4(OC0), 18(OC1B), 19(OC1A) & 21(OC2). Then pin 4

& 19 are connected to motor B while pin 18 & 21 are connected to motor A. We use

Atmega16L because Raspberry Pi can generate only single PWM which cannot serve

our purpose.


Figure 4.3: Logitech C210.

4.4 Motor

Parameters Value

Rated Voltage 24 V

No Load Current 180 mANo Load Speed 6000 rev/min

Load Current 1000 mALoad Speed 4500 r/min

Output Power 13.5 W

Stall Current 4000 mALoad Torque 300 gf.cm

Small Torque 1200 gf.cm

Table 4.3: Motors Standard Parameters

4.5 Motor Driver (H-Bidge)

H-bridge consists of four switching element, with the motor at the center in an H-like

configuration as shown in figure 4.4. It allows to control the speed as well as direction of

motor. As motors are mostly controlled by micro controller, it provides the instructions

to the motors but cannot provide the power required to drive the motors. An H-bridge

circuit inputs the micro controller instructions and amplifies them to drive motor. The

H-bridge made takes in the small electrical signal and results in high power output for

mechanical motor. H-Bridge and micro controller is linked with the help of apto coupler,

it is a component that transfers electrical signals between two isolated circuits by using

light. Opto-isolators prevent high voltages from affecting the system receiving the signal

4.5.1 Direction Control

Most DC Motors can rotate in two directions depending on how the battery is connected

to the motor. An H-Bridge circuit allows a DC motor to be run in any direction with

a low level logic input signal. Here switches represents the electronic Power MOSFETs

which are used for switching. In the figure 4.4 if switch 1 and 4 are closed then motor


Figure 4.4: Typical H-Bridge.

rotates in forward direction and if switch 2 and 3 are made closed then motor change its

direction and rotates in reverse direction as shown in figure 4.5. So by using a simple

H-Bridge we can easily control the direction of rotation of motors.

Figure 4.5: Direction Control of H-Bridge.

4.5.2 Speed Control

Speed can be controlled by using PWM. For that purpose we generate four PWMs with

the help of Atmega16L micro controller as shown in appendix A.1. The main principle

is to control the power by varying the duty cycle so that the conduction time to the

load is controlled. The main advantage of PWM is that power loss in the switching

devices is very low. When a switch is off there is no current, and when it is on, there is

almost no voltage drop across the switch. So losses are negligible. If we use an analog

input to control the speed of motor, it will not produce significant torque at low speeds.

The magnetic field created by the small current will be too weak to turn the rotor.

While PWM current can create short pulses of magnetic flux at full strength, which can


turn the rotor at extremely slow speeds. Micro controllers offer simple commands to

vary the duty cycle and frequencies of the PWM control signal. PWM is also used in

communications to take advantage of the higher immunity to noise provided by digital

signals. So by getting these pulses generated by a micro controller we can increase the

efficiency, accuracy and thus the reliability of the system. H-Bridge circuit consists of

following ICs:

IR2103 (Half-Bridge Driver): It is eight pin high voltage, high speed powerMOSFET and IGBT drivers with dependent high and low side referenced output

channels.

IRF3205: Advanced HEXFET Power MOSFETs from International Rectifierutilize advanced processing techniques to achieve extremely low on-resistance per

silicon area.

IN4148: It is a high-speed switching diode fabricated in planar technology, andencapsulated in hermetically sealed leaded glass.

TLP250: It is an apto coupler.

Appendix A.2 shows the circuit diagram and figure 4.6 shows the hardware of H-Bridge

Motor drivers.

Figure 4.6: H-Bridge Motor Driver Hardware.

4.6 Mechanical Structure

Mechanical Structure consists of steel sheet and Teflon tyres and all the other circuitry

s placed on it as shown in figure 4.7.


Figure 4.7: Hardware.

Chapter 5

Software Implementation

We have mounted a camera in front of the prototype, to get a live video feed into the

processing module i.e laptop. Image processing can be done using two below mentioned

options:

C, Java or Python language

MATLAB

5.1 Comparison

In C, Java or Python language, we have to start from the scratch, create our own

methods and then use them. Although, it is faster than MATLAB but it is much more

complex than MATLAB. So we first used MATLABs built in SIMULINK library to

take a start.

Using MATLAB, we perform edge detection on the images which gives us a binary

matrix containing 1s wherever there is an edge and 0s otherwise. From these processed

images, we determined the obstacle. One of these processed images is shown below in

figure 5.1 where white portion indicates an edge and the black portion indicates that

there are no edges.

Figure 5.1: Original and Edge Detected Image.

16

Chapter 4. Software Implementation 17

5.2 Implementation Using Simulink

For a start, we used the SIMULINK library of MATLAB. In simulink, we made use of

the IMAGE AND VIDEO PROCESSING TOOLBOX. The simple Block diagram is

given in figure 5.2.

Figure 5.2: Edge Detection Using MATLAB.

For proceeding further, these binary matrices had to be stored in particular variables

which are further forwarded to next M-file which determines whether an obstacle is

present or not. This interlink could not be achieved in SIMULINK. So, we wrote our

own M-files which take input and process it. Moreover, the unnecessary portion of the

video was also removed to reduce the chunk of unnecessary computations done i.e only

that portion of the video was kept which covers only the frontal view of the vehicle.

5.3 Block Diagrams

Figure 5.3 & 5.4 shows the block diagrams of Edge Detection Algorithm and complete

ACM in MATLAB respectively.


Figure 5.3: Edge Detection Algorithm.

Figure 5.4: Block Diagram of ACM in MATLAB.


After successful implementation ofACM inMATLAB,We moved to Python and OpenCV

because they are faster and laptop can be replaced by a small board(Raspberry Pi). Fig-

ure 5.5 & 5.6 shows the block diagrams of ACM Algorithm using Pyhton and division

of input image respectively. Due to excessively noisy results in the background subtrac-

tion algorithm, we had to switch to Canny Edge Detection algorithm to obtain Binary

Images that only include obstacles as edges and no background noise(floor cracks) are

detected as obstacles. Figure 5.7 shows the block diagrams of ACM using Canny Edge

Detection algorithm.

Figure 5.5: Block Diagram of pyhton Algorithm.


Figure 5.6: Division of input image.

Figure 5.7: Canny Edge Detection Algorithm.

Chapter 6

Conclusion and Future Directions

21

Appendix A

Circuit Diagram

A.1 PWM Generation Circuit

Figure A.1: PWM Generator for H-Bridge.

22

Appendix A. Circuit Diagram 23

A.2 H-Bridge Circuit

Figure A.2: H-Bridge Simplified Circuit.

Figure A.3: H-Bridge Complete Circuit.

Appendix B

Codes

B.1 Code for PWM Generation in ATMEGA16L

B.2 Code for ACM in Raspberry Pi

import cv2.cv as cv

import time

import numpy as np

from scipy import stats

import RPi.GPIO as GPIO

cap = cv.CaptureFromCAM(0)

GPIO.setmode(GPIO.BCM)

GPIO.setup(4,GPIO.OUT)



cv.SetCaptureProperty(cap,cv.CV_CAP_PROP_FRAME_HEIGHT,240)

cv.SetCaptureProperty(cap,cv.CV_CAP_PROP_FRAME_WIDTH,320)

start-time = time.time()

i=0

while True:

img = cv.QueryFrame(cap)

24

Appendix B. Codes 25

grey = cv.CreateImage(cv.GetSize(img),img.depth,1)

cv.CvtColor(img,grey,cv.CV_RGB2GRay)

ul = grey[0:120,0:160]

cv.ShowImage("crop",ul)

ll = grey[120:240,0:160]

cv.ShowImage("crop2",ll)

ur = grey[0:120.160:320]

lr = grey[120:240,160:320]

cv.AbsDiff(ll,ul,ul)

cv.AbsDiff(lr,ur,ur)

array_ul = np.array(ul)

array_ur = np.array(ur)

stats.threshold(array_ul,1,70,0)

stats.threshold(array_ul,71,255,1)

stats.threshold(array_ur,1,70,0)

stats.threshold(array_ur,71,255,1)

left = np.count_nonzero(array_ul)

right = np.count_nonzero(array_ur)

if(left-right100)

print "right"

GPIO.output(17,True)

GPIO.output(18,False)


if(right-left>100)


print "left"



GPIO.output(4,True)

if cv.WaitKey(10) == 27

break

i +=1

Due to excessively noisy results in the background subtraction algorithm, we had to

switch to Canny Edge Detection algorithm to obtain Binary Images that only include

obstacles as edges and no background noise(floor cracks) are detected as obstacles.

import cv2.cv as cv

import time

import numpy as np

import RPi.GPIO as GPIO

cap = cv.CaptureFromCAM(0)

GPIO.setmode(GPIO.BCM)




cv.SetCaptureProperty(cap,cv.CV_CAP_PROP_FRAME_HEIGHT,240)

cv.SetCaptureProperty(cap,cv.CV_CAP_PROP_FRAME_WIDTH,320)

start-time = time.time()

while True:

frame = cv.QueryFrame(cap)

gray = cv.CreateImage(cv.GetSize(frame),frame.depth,1)

cv.CvtColor(frame,grey,cv.CV_RGB2GRAY)

blur = cv.CreateImage(cv.GetSize(gray),cv.IPL_DEPTH_8U,grey.channels)

cv.Smooth(grey,blur,cv.CV_GAUSSIAN,5,5)


canny = cv.CreateImage(cv.GetSize(blur),blur.depth,blur.channels)

cv.Canny(blur,canny,30,150,3)

l_img = canny[0:240,0:160]

cv.ShowImage(left,l_img)

r_img = canny[0:240,161:320]

cv.ShowImage(right,r_img)

left_array = np.array(l_img)

right_array = np.array(r_img)

left = np.count_nonzero(left_array)

right = np.count_nonzero(right_array)

if(left-right==0)



GPIO.output(4,True)

if(left-right>0)




if(left-right

References

[1] WHO: Global status report on road safety 2013 . http://www.who.int/violence_

injury_prevention/road_safety_status/2013/en/index.html.

28

AcknowledgmentsList of FiguresList of TablesAbbreviationsAbstract1 Introduction1.1 Obstacle Detection Techniques1.1.1 IR Sensor1.1.2 Sonar Sensor1.1.3 Laser Scanning1.1.4 Image Processing

2 Motivations and Problem Statement2.1 Motivations2.1.1 Risky Jobs2.1.2 Labor Shortage2.1.3 Reliability

2.2 Problem Statement

3 Proposed Approach4 Hardware Implementation and Design4.1 Raspberry Pi Board4.1.1 Hardware4.1.2 Operation4.1.2.1 Operating System4.1.2.2 Camera4.1.2.3 GPIO4.1.2.4 Display4.1.2.5 PWM Generation

4.2 Camera4.3 Atmega16L4.4 Motor4.5 Motor Driver (H-Bidge)4.5.1 Direction Control4.5.2 Speed Control

4.6 Mechanical Structure

5 Software Implementation5.1 Comparison5.2 Implementation Using Simulink5.3 Block Diagrams

6 Conclusion and Future DirectionsA Circuit DiagramA.1 PWM Generation CircuitA.2 H-Bridge Circuit

B CodesB.1 Code for PWM Generation in ATMEGA16LB.2 Code for ACM in Raspberry Pi

References

Documents

Anti Collision Mechanism in Vehicles