OBJECT RECOGNITION AND SORTING ROBOT

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OBJECT RECOGNITION AND SORTING

ROBOT

By

Umang Sharma

Km Swati

College of Engineering

University of Petroleum & Energy Studies

Dehradun

May, 2012


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Object Recognition and Sorting Robot

A project report submitted in partial fulfilment of the requirements for the Degree ofBachelor of Technology(Electronics Engineering)

By

Umang Sharma

Km Swati

Under the guidance of

Mr. Amit Kumar MondalDoctoral Research Fellow

Electronics Electrical and Instrumentation Department, COES

Approved

.. Director

College of Engineering

University of Petroleum & Energy Studies

DehradunMay, 2012


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CERTIFICATE

This is to certify that the work contained in this report titled Object Recognition

and Sorting Robot has been carried out Umang Sharma and Km Swati by. undermy supervision and has not been submitted elsewhere for a degree.

..

.

.

.

Date


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Abstract/ Synopsis

Color is the most common feature to distinguish between objects, sorting,

recognizing and tracking. Colour detection has proven to be useful for face

detection, localization and tracking. The developed algorithm can be divided in to

three main parts. Detection recognition of object and movement of the robot. The

objective of the project is to separate the different coloured objects from a set. This

technology is used in an industry where the objects moving through a conveyer

belt or by human beings can be separated using a colour detecting robot. The

detection of the colour is done through image processing technique using

MATLAB. This project consists of a MATLAB based movable robot for separate

the different coloured objects from a set. The MATLAB based system recognizes

the colour and sends command to the controller. The controller, using the incoming

signal controls the movements of the robot and arm. The robot consist of four DC

motors and one free wheel two dc motors are used for arm in which one for the

base and second for the gripper and other two motors and free wheel is used for

movement of the robot .


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Acknowledgement

The project bears the imprints of the efforts extended by many people to whom we are deeply

indebted. We take this opportunity to express great sense of gratitude to Mr Adesh kumar for

providing us the opportunity to carry out this project.

We also extend our heartiest thanks to our project guide Mr. Amit Kumar Mondal, for his

invaluable guidance, support and encouragement during working on the project Object

Recognitionand Sorting Robot for Material Handling in Packaging and Logistic Industries. His

effective planning, coordination, skill, knowledge and experience have made it possible to made

successfully progress in the project within the stipulated time. We are also indebted to all the

faculty members of Electronic Department as a whole and everything we got from here.

I also like thank to my friends and everyone who extended a helping hand towards me.

At the last but not the least, a remembrance to the God Almighty,without whose blessings, the

thoughts about the project will not go forth

Km Swati

Umang Sharma


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Table of content

S. No. Name of Content Page

No.

1 Introduction 9

2 Literature Review 11

3 Theortical development 16

4 Methodology 17

5 Image Acquistion 18

6 Color Recognition 19

7 Flow Chart of Process 20

8 Hardware Development 21

9 Development of System 23

10 Software Development 31

11 AVR Studio 31

12 MATLAB 35

13 Proteus 36

14 Programming in MATLAB 38

15 Programme for Motor Control 41

16 Appliacations 42

17 Result and Discussions 43

18 Conclusion and Suggestion 44

19 Refrences 45

20


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List of figures

S.No. Name of Figure Page

No.

1 Flow Diagram of Methodology 18

2 Flow Diagram of Image Acquistion 18

3 Flow Diagram of Color Recognition 20

4 Flow Chart of the Process 20

5 Basic Diagram of Hardware Development 21

6 Block Diagram of Hardware Development 22

7 DB9 Connector 23

8 MAX 232 25

9 ATmega 16 26

10 Pin Description 27

11 L293D Connection 28

12 Crystal Oscillator 29

13 DC Motor 29

14 Power Supply 30

15 Software Development Cycle 32

16 Coding in AVR Studio 33

17 Compiling in AVR Studio 34

18 Burning of Program in Micro Controller 34

19 MATLAB Desktop 36

20 Systematic Diagram Using Proteus 37

21 Programming in MATLAB 40


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Nomenclature

1) V: Volt2) DC: Direct Current3) AC: Alternating current4) Matlab: Matrix laboratory5) IR sensor: Infrared sensors6) USB: Universal serial Bus7) IC: Integrated Circuit8) RGB: Red, green, blue9) PCB: Printed circuit board10) RISC: Reduced instruction set computing11) CMOS: Complementray metal oxide semiconductor12) I/O: Input/output13) A/D: Analog/digital


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Chapter1: Introduction

From the 1960s until the present, the field of image processing has grown vigorously. In addition

to applications in medicine and the space program, digital image processing techniques now are

used in a broad range of applications. Computer procedures are used to enhance the contrast or

code the intensity levels into colour for easier interpretation of X-rays and other images used in

industry, medicine, and the biological sciences. Geographers use the same or similar techniques

to study pollution patterns from aerial and satellite imagery. Image enhancement and restoration

procedures are used to process degraded images of unrecoverable objects or experimental results

too expensive to duplicate.

Many real world applications require real-time image processing like motion and color

recognition. Performance of a color recognition system should be fast enough so that objects in

video can be detected and processed in real time. Once object in a video is detected, object

tracking, image data mining, semantic meaning extraction, and other video and image processing

techniques can be performed. To extract the maximum benefit from this recorded digital data,

detection of any object from the scene is needed without engaging any human eye to monitor

things all the time. Real-time colour detection and Recognition of images is a fundamental step

in many vision systems[1].

Image processing is a form of signal processing in which the input is an image, such as a

photograph or video frame. The output of image processing may be either an image or, a set of

characteristics or parameters related to the image. Most image-processing techniques involve

treating the image as a two-dimensional signal and applying standard signal-processing


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techniques to it. This project aims at processing the real time images captured by a Webcam for

motion detection and Color Recognition using MATLAB programming. The results of this

processing can be used in sense the particular colored block and automatically pick up the block

and place it into a bin according to its color.


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Chapter2: LITERATURE SURVEY

1.1 This paper appears in: Real-time capable system for hand gesture recognition Using hidden

Markov models in stereo color image sequences.

Issue Date: 2008

Author: Elmezain, Mahmoud, Al-Hamadi , Ayoub Michaelis , Bernd.

ISBN: 978-80-86943-14-5

Product type: Article

Abstract

This paper proposes a system to recognize the alphabets and numbers in real time from color

image sequences by the motion trajectory of a single hand using Hidden Markov Models

(HMM). Our system is based on three main stages; automatic segmentation and preprocessing of

the hand regions, feature extraction and classification. In automatic segmentation and

preprocessing stage, YCbCr color space and depth information are used to detect hands and face

in connection with morphological operation where Gaussian Mixture Model (GMM) is used for

computing the skin probability. After the hand is detected and the centroid point of the hand

region is determined, the tracking will take place in the further steps to determine the hand

motion trajectory by using a search area around the hand region. In the feature extraction stage,

the orientation is determined between two consecutive points from hand motion trajectory and

then it is quantized to give a discrete vector that is used as input to HMM. The final stage so-

called classification, Baum-Welch algorithm (BW) is used to do a full train for HMM

parameters. The gesture of alphabets and numbers is recognized by using Left-Right Banded

model (LRB) in conjunction with Forward algorithm. In our experiment, 720 trained gestures are
https://otik.uk.zcu.cz/browse?type=author&value=Al-Hamadi%2C+Ayoubhttps://otik.uk.zcu.cz/browse?type=author&value=Al-Hamadi%2C+Ayoub


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used for training and also 360 tested gestures for testing. Our system recognizes the alphabets

from A to Z and numbers from 0 to 9 and achieves an average recognition rate of 94.72%[2]

1.2 This paper appears in: A real-time color feature tracking system using color histograms

Issue Date: Date of Conference: 17-20 Oct. 2007

Author(s): Jung Uk ChoSungkyunkwan Univ, Suwon Seung Hun Jin; Xuan Dai Pham;

Dongkyun Kim; Jae Wook Jeon

Page(s): 11631167

Product Type: Conference Publications

Abstract

Color feature tracking is based on pattern matching algorithms where the appearance of the

target is compared with a reference model in successive images and the position of the target is

estimated. The major drawback of these methods is that such operations require expensive

computation power. It is bottleneck to implement real-time color feature tracking system. The

probabilistic tracking methods have been shown to be robust and versatile for a modest

computational cost. However, the probabilistic tracking methods break down easily when the

object moves very fast because these methods search only the regions of interest based on the

probability density function to estimate the position of the moving object. In this paper, we

propose a real-time color feature tracking circuit. We propose a window-based image processing

structure to improve the processing speed of the tracking circuit. The tracking circuit searches all

regions of the image to perform a matching operation in order to estimate the position of the

moving object. The main results of our work are that we have designed and implemented a

physically feasible hardware circuit to improve the processing speed of the operations required
http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Jae%20Wook%20Jeon.QT.&newsearch=partialPrefhttp://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Jae%20Wook%20Jeon.QT.&newsearch=partialPref


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for real-time color feature tracking. Therefore, this work has resulted in the development of a

real-time color feature tracking system employing an FPGA (field programmable gate array)

implemented circuit designed by VHDL (the VHSIC hardware description tanguage). Its

performance has been measured to compare with the equivalent software implementation.[3]

1.3 This paper appears in: Automatic Number Plate Recognition System for Vehicle

Identification Using Optical Character Recognition.

Issue Date: Date of Conference: 17-20 April 2009

Author(s): Qadri, M.T. Dept. of Electron. Eng., Sir Syed Univ. of Eng. & Technol., Karachi,

Pakistan ,Asif, M.

Page(s): 335-338


Abstract

Automatic number plate recognition (ANPR) is an image processing technology which uses

number (license) plate to identify the vehicle. The objective is to design an efficient automatic

authorized vehicle identification system by using the vehicle number plate. The system is

implemented on the entrance for security control of a highly restricted area like military zones or

area around top government offices e.g. Parliament, Supreme Court etc. The developed system

first detects the vehicle and then captures the vehicle image. Vehicle number plate region is

extracted using the image segmentation in an image. Optical character recognition technique is

used for the character recognition. The resulting data is then used to compare with the records on

a database so as to come up with the specific information like the vehiclepsilas owner, place of

registration, address, etc. The system is implemented and simulated in Matlab, and it
http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Qadri,%20M.T..QT.&searchWithin=p_Author_Ids:37320522100&newsearch=truehttp://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Asif,%20M..QT.&searchWithin=p_Author_Ids:37658214100&newsearch=truehttp://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Asif,%20M..QT.&searchWithin=p_Author_Ids:37658214100&newsearch=truehttp://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Asif,%20M..QT.&searchWithin=p_Author_Ids:37658214100&newsearch=truehttp://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Qadri,%20M.T..QT.&searchWithin=p_Author_Ids:37320522100&newsearch=true


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performance is tested on real image. It is observed from the experiment that the developed

system successfully detects and recognize the vehicle number plate on real images.[4]

1.4 Thispaper appears in: Recognition, Analysis, and Tracking of Faces and Gestures in Real-

Time Systems

Date of Conference: 2001

Author(s): Jianhuang Lai Dept. of Comput. Sci., Hong Kong Baptist Univ., China Yuen,

P.C.; Wensheng Chen; Shihong Lao;

Page(s): 168-174


Abstract

Addresses the problem of facial feature point detection under different lighting conditions. Our

goal is to develop an efficient detection algorithm, which is suitable for practical applications.

The problems that we need to overcome include (1) high detection accuracy, (2) low

computational time and (3) nonlinear illumination. An algorithm is developed and reported in the

paper. One of the key factors affecting the performance of feature point detection is the accuracy

in locating face boundary. To solve this problem, we propose to make use of skin color, lip color

and also the face boundary information. The basic idea to overcome the nonlinear illumination is

that, each person shares the same/similar facial primitives, such as two eyes, one nose and one

mouth. So the binary images of each person should be similar. Again, if a binary image (with

appropriate thresholding) is obtained from the gray scale image, the facial feature points can also

be detection easily. To achieve this, we propose to use the integral optical density (IOD) on face

region. We propose to use the average IOD to detect feature windows. As all the above-
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7480http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7480http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Jianhuang%20Lai.QT.&searchWithin=p_Author_Ids:37274695700&newsearch=truehttp://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Yuen,%20P.C..QT.&searchWithin=p_Author_Ids:38264172900&newsearch=truehttp://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Yuen,%20P.C..QT.&searchWithin=p_Author_Ids:38264172900&newsearch=truehttp://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Wensheng%20Chen.QT.&searchWithin=p_Author_Ids:37279196500&newsearch=truehttp://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Shihong%20Lao.QT.&searchWithin=p_Author_Ids:37271029600&newsearch=truehttp://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Shihong%20Lao.QT.&searchWithin=p_Author_Ids:37271029600&newsearch=truehttp://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Wensheng%20Chen.QT.&searchWithin=p_Author_Ids:37279196500&newsearch=truehttp://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Yuen,%20P.C..QT.&searchWithin=p_Author_Ids:38264172900&newsearch=truehttp://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Yuen,%20P.C..QT.&searchWithin=p_Author_Ids:38264172900&newsearch=truehttp://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Jianhuang%20Lai.QT.&searchWithin=p_Author_Ids:37274695700&newsearch=truehttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7480http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7480


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mentioned techniques are simple and efficient, the proposed method is computationally effective

and suitable for practical applications. 743 images from the Omron database with different facial

expressions, different glasses and different hairstyle captured indoor and outdoor have been used

to evaluate the proposed method and the detection accuracy is around 86%. The computational

time in Pentium III 750 MHz using matlab for implementation is less than 7 seconds.


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Chapter 3 Theoretical Development

1.1 Technical Specifications

Domain : Embedded System

Software : AVR, MATLAB and Proteus

Microcontroller : Atmega 16

Power Supply : 12V from the battery and 5V from the board

Communication : Wired

Application : Industry process, Home automation


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1.2 Methodology

Most image-processing techniques involve treating the image as a two-dimensional signal and

applying standard signal-processing techniques to it. The video captured by the camera is being

processed by the MATLAB program that helps in color recognition. The results of this

processing can be used in numerous security applications such as intrusion detection, Spy robots,

fire alarm, person finder,sorting of objects etc. Generally signal processing is used in the analysis

of the colour of an object. In this project the detection of different colors is done through image

processing technique using MATLAB. The goal of the project is to develop Bot. Bot is a typical

model used to pick and place the desired color objects from one location to another. This robot is

used in sorting the objects in a mixture of different color objects. The project consists of a

MATLAB based robotic arm and a controller for controlling the mechanical movements. An

Objrec algorithm was developed in MATLAB to recognize the color and send command to the

controller using serial communication. The controller, using the incoming signal controls the

movements of the robot. The robot consist of two DC motors one for the base and another for the

gripper. The controller that was used is ATMEGA 16. RS232 communication was used for

MATLAB to communicate with the microcontroller.


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Figure 1: Flow diagram of methodology

1.3 Image Acquisition

The first stage of any vision system is the image acquisition stage. After the image has been

obtained, various methods of processing can be applied to the image to perform the many

different vision tasks required today.

Figure 2: Flow diagram of Image Acquisition


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1.3.1 Algorithm for Image Acquisition

1Install the image acquisition device by installing the frame grabber board in your computer.

These devices directly connect your computer via a USB or FireWire port. After installing and

configuring your image acquisition hardware, start MATLAB on your computer.

2. With the comment imaqhwinfo we can get all information of our camera.

3. Create a Video Input Object. To create a video input object, use the video input function at

the MATLAB prompt.

4. Acquire Image Data.

5. Start Acquiring Image or Video.

1.4 Need for colour detection

The colour detection is of very much importance to human as we daily use colour to differentiate

the objects in the environment, recognize them and communicate using these information.

Analysis of colour in the image processing is basically signal processing. Image Processing is a

technique to improve the images received from cameras acting as sensors placed on robots,

satellites, space probes and aircrafts or images taken in daily life for various applications.

1.5 Color Recognition

Color plays an important role in image processing. Each image is composed of an array of M*N

pixels with M rows and N columns of pixels. Each pixel contains a certain value for red, green

and blue Varying these values for red, green, blue (RGB) we can get almost any color.

Images:

picture (row, column, rgb value)


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Videos:

frames(row, column, rgb value, frame)

Figure3: Flow diagram of Color Recognition In MATLAB

1.6 Flow chart for the process

Figure4: Flow chart of the process


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2.1 Hardware Development

Large volume of data is produced when camera is used as a sensor. Other sensors give out he

output in terms of 0s or 1s like position sensor, encoders, IR sensors etc. The power supply

supplies the power of 5V to the controller to operate, which include a bridge rectifier and a

voltage regulator , a capacitor and an LED. ATMEGA 16 is the microcontroller which receives

the commands from the MATLAB and sends the commands to the L293D for driving the

motors. MAX 232 IC is used for serial communication in order to communicate with the PC. A

USB to Serial cable is used in Between MAX 232 and the PC for the flow of data. To drive the

two DC motors the IC L293D is used.

Figure 5: Basic diagram of hardware development

The hardware implementation deals in:

Drawing the schematic on the plane paper according to the application

Testing the schematic design over the breadboard using the various ICs to find if the

design meets the objective

Designing the PCB layout of the schematic tested on the breadboard.

Finally preparing the board and testing the designed hardware

Hardware development of Bot is divided into two parts.

Interfacing section


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Power supply

Figure 6 : Development Board

The hardware board as shown in Figure 2 consists of:

Power supply

ATMEGA 16

MAX 232

L293D

DB9

USB to serial cable

Focus range: 3 cm to infinity

Clip type mounting to clamp on any surface

Active night vision with backlit LEDs

Integrated microphone for sound recording


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Figure 7: Block diagram of hardware development

Once the colour is detected, the microcontroller will initiate the following actions on the robot.

Gripper open

Gripper close

Left

Right

2.2 Development of the System

A simple approach for developing object reorganization system is shown below:

Decide the ideal position of the object with respect to the camera

The distinguishing feature of the object to be picked is to be figured out.

Deciding the robots movement as planned

2.3 Hardware description:

2.3.1 DB Connector:The DB9-USB-RS232 connector can be used to upgrade RS232 port to active USB port

without the need to redesign the PCB. These active connectors contain all the USB to

RS232 (and vice-versa) conversion electronics and are designed to fit directly into the same

PCB footprint as a PC compatible RS232DB9 connector. The FTDI DB9-USB-RS232

connectors come in two types DB9-USB-RS232-M and DB9-USB-RS232-F . A DB9-USB-


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RS232-M canbe used to replace male DB9 connector that is wired in a PC compatible

RS232manner. A DB9-USB-RS232-F can be used to replace female DB9 connector that is

wired in a PC compatible RS232manner. The purpose of the modules is to provide

simple method of adapting legacy serial devices with RS232 interfaces to modern USB ports

by replacing the DB9 connector with miniaturized module which is closely resembles a DB9

connector. This is accomplished by incorporating the industry standard FTDI FT232R USB-

Serial Bridge IC plus the required level shifters inside the module.

Fig 7: DB connector

2.3.2 MAX232:The MAX232 is an integrated circuit that converts signal from an RS-232 serial port to

signal suitable for use in TTL compatible digital logic circuits. The MAX232 is a dual

driver/receiver and typically converts the RX, TX, CTS and RTS signals. The drivers provide

RS-232 voltage level outputs (approx. 7.5 V) from a single + 5 V supply via on-chip charge

pumps and external capacitors. This makes it useful for implementing RS-232 in devices

that do not need any voltages outside the 0 V to + 5 V range, as power supply design

does not need to be made more complicated just for driving the RS-232 in this case. The

receivers reduce RS-232 inputs (which may be as high as 25 V), to standard 5 V TTL


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levels. These receivers have a typical threshold of 1.3 V , and a typical hysteresis of 0.5

V . Later MAX232A is backwards compatible with the original MAX232 but may operate at

higher baud rate and can use smaller external capacitors 0.1 F in place of the 1.0 F

capacitors used with the original device. The newer MAX3232 is also backwards

compatible, but operates at a broader voltage range, from 3 to 5.5 V pin to pin compatible:

ICL232, ST232, ADM232, HIN2.

Fig 8: Max 232

2.3.3 ATmega 16:The ATmega16 is a low-power CMOS 8-bit microcontroller based on the AVR enhanced RISC

architecture. By executing powerful instructions in a single clock cycle, the ATmega16 achieves

throughputs approaching 1 MIPS per MHz allowing the system designer to optimize power

consumption versus processing speed.

(a)Features :High-performance, Low-power AVR 8-bit Microcontroller.

130 Powerful InstructionsMost Single Clock Cycle Execution

32 x 8 General Purpose Working Registers


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Fully Static Operation

Up to 16 MIPS Throughput at 16 MHz

On-chip 2-cycle Multiplier

512 Bytes EEPROM

512 Bytes Internal SRAM

Fig 9: ATmega 16

(b)Pin description of ATmega 16:VCC:Digital supply voltage.

GND: Ground.

PORT(PA7-PA0): Port A: serves as the analog input to A/D converter. Port A also serves as

an 8-bit bidirectional I/O port, if the A/D port is not used.

PORT B(PB7-PB0): Port B is an 8-bit bidirectional I/O port.

PORT C(PC7-PC0):Port C ia an 8-bit bidirectional I0O port with external pull up resistors.

PORT D(PD7-PD0):Port D is an 8-bit bidirectional I/O port with external pull up resistors.

RESET-Reset input. A low level on this pin for longer than maximum pulse length will

generate a reset even if the clock is not running.


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XTAL1- Input to the inverting oscillator amplifier and input to the internal clock operating

circuit.

XTAL2- Output from the inverting oscillator amplifier.

AVCC:- AVCC is the supply voltage pin for port A and the A/D converter.

AREF:-It is the analog reference pin to the A/D convertor.

Fig 10: Pin discription

2.3.4 L293D:

To control a dc motor we have to first convert digital output to a signal which can run motors so

we have used the H-bridge. Here we have a simple Hbridge circuit. An H-bridge is an

electronic circuit which enables DC electric motors to be run forwards or backwards. These

circuits are often used in robotics. Hbridges are available as integrated circuits, or can be built

from separate components.

Truth Table

High Left High Right Low Left Low Right Description

On Off Off On Motor runs clockwise


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Off On On Off Motor runs anti-clockwise

On On Off Off Motor stops or decelerates

Off Off On On Motor stops or decelerates

Fig 11 : L293D connection

2.3.5 Crystal Oscillator:

A crystal oscillator is an electronic oscillator circuit that uses the mechanical resonance of a

vibrating crystal of piezoelectric material to create an electrical signal with a very precise

frequency. This frequency is commonly used to keep track of time (as in quartz wristwatches), to

provide a stable clock signal for digital integrated circuits, and to stabilize frequencies for

radio transmitters and receivers. The most common type of piezoelectric resonator used is the

quartz crystal, so oscillator circuits designed around them became known as "crystal

oscillators.


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Fig 12: Crystal oscillator

2.3.6 DC Motors:

DC motor is a machine that produces rotation. It is an arrangement of coils and magnets that

converts elecrtric current into mechanical rotation. In robotics applications,they are preffered

over AC motors as the motor and the complete circuit require same kind of supply.i.e DC supply.

Features:

Easy to control speed.

Easy to control torque.

Motors having external gear arrangement attached with motor.

It has a gear box thet increases torque decreases speed.

Most commonly used in robotics as they are having considerable torque.


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Fig 13: DC motor

2.3.7 Power supply

Generally the battery is source of power to robot. The battery power is not sufficient for driving

the motors of the robot, hence power supply unit should be added to the circuit. Step down

transformer is used for converting the higer voltage into lower voltage. Bridge rectifer is used for

converting AC voltage into plusating DC voltage and RC rectifer is used for converting plusating

DC into pure DC voltage.

Figure14: Power supply


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Chapter 4 Experimental/ Computational

1. Software Development

1.1AVR Studio:A microcontroller often serves as the brain of a mechatronic system. Like a mini, self-

contained computer, it can be programmed to interact with both the hardware of the system and

the user. We use avr studio for programming of our microcontoller and use c language for the

coding . There are several ways that we can write, compile, and download a program to the

ATmega16 microcontroller. WinAVR consists of a suite of executable, open source software

development tools for the Atmel AVR series of RISC microprocessors hosted on the Windows

platform. It includes the GNU GCC compiler for C and C++, which is sometimes referred to as

avr-gcc.

The coding is done in AVR Studio4 in embedded C. The actions performed by the robot are

written in ATMEGA16 microcontroller. The following steps are involved in the software

development:

Coding/debugging

Compiling

Burning


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Figure 15: software development cycle

1.1.1 Coding / debugging:In a high-level language (such as C, or Java), a compiler for a high level language helps to

reduce production time. T o program the microcontrollers the WinAVR was used. Although

inline assembly was possible, the programming was done strictly in the C language. The source

code has been commented to facilitate any occasional future improvement and maintenance.

WinAVR is a suite of executable, open source software development tools for the Atmel

AVR series of RISC microprocessors hosted on the Windows platform. Coding / Debugging:

it includes the GNU GCC compiler for C and C++. WinAVR contains all the tools for

developing on the AVR. This includes AVR-gcc (compiler), AVR-gdb (debugger) etc. Test

Source Code has been written in C Language to test the microcontroller .

High level languages such as C, Java or assembly language are used for coding and debugging.

For Bot coding is done in AVR studio4 using embedded C language. The code is written to move

the motors of the robot according to the image acquired.


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Figure 16: coding in AVR studio

1.1.2 CompilingThe compilation of the C program converts it into machine language file (.hex). This is the

only language the microcontroller will understand, because it contains the original program

code converted into a hexadecimal format. During this step there were some warnings

about eventual errors in the program.

A compiler for a high level language helps to reduce production time. To program the

microcontroller WinAvr was used. Although inline assembly was possible, the programming was

done in strictly in C. A source code for USART has also been included. The microcontroller


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understands only the machine level language.

Figure 17 : Compiling in AVR Studio

1.1.3 BurningBurning the machine language file into the micro controllers program memory is achieved with

the dedicated programmer, which is attached to the PC peripheral. PCs serial port has been used

for this purpose.

Figure 18: Buring of Programme in Microcontroller


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1.2Matlab:Matlab = matrix laboratory

It is a numerical computing environment and fourth generation programming language ,

developed by Mathworks . Matlab allows matrix manipulations , plotting of functions and data

implementation of algorithms creation of user interfaces and interfacing with programs written in

other languages including c , c++ , Java and Fortran. It is an interactive program for numerical

computation and data visualization.

Although MATLAB is intended primarily for numerical computing , an optional toolbox uses the

MuPAD symbolic engine, allowing access to symbolic computing capabilities . An additional

package simulink adds graphical multi-domain simulation and model-based design for dynamic

and Embedded system.

The MATLAB desktop is the main MATLAB application window. As Fig below shows, the

desktop contains five sub-windows: the Command Window, the Workspace Browser, the

Current Directory Window, the Command History Window, and one or more Figure Windows,

which are shown only when the user displays a graphic.


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Figure 19 : MATLAB Desktop

Matlab commands description

A) Syntax: imaqtoolDescription: Imaqtool launches an interactive GUI to allow you to explore, configure, and

acquire data from installed and supported image acquisition devices.

B) Syntax: vid = videoinput('winvideo',1);Description: Vid is called a video input object( vid is a custom MATLAB class.)

Winvideo is the adaptor name for image acquisition hardware.

1 is the device ID (given by imaqhwinfo)

C) Syntax: Set(vid)Description: Use the set function to display all configurable device object properties.


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D) Syntax:Get(vid)Description: Use the get function to return the current device object property values.

E) Syntax : inspect(vid)Description: Use the image acquisition property editor to view and edit the device object'sproperties.

F) Syntax: imshow(fiugre name);Description: The imshow function displays the image in a MATLAB figure window.

G)Syntax: triggerconfig(obj,type)Description: configures thevalue of the TriggerType property of the video input object obj to

the value specified by the text string type.

H)Syntax: start(vid);Description: When you start an object, you reserve the device for your exclusive use and lock

the configuration. Thus, certain properties become read only while running.

I)Syntax: rgb2ycbcr();

Description: This command converts the RGB values in map to the YCbCr color

space. map must be an M-by-3 array. ycbcrmap is an M-by-3 matrix that contains the YCbCr

luminance (Y) and chrominance (Cb and Cr) color values as columns. Each rowin ycbcfmap

represents the equivalent color to the corresponding row in the RGB colormap, map.

J)Syntax: YCBCR = rgb2ycbcr(RGB)Description: This command converts the truecolor image RGB to the equivalent image in the

YCbCr color space. RGB must be a M-by-N-by-3 array.


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K)Syntax: getdata(obj);

Description: Extracts the number of samples specified by the SamplesPerTriggerproperty for

each channel contained byobj. data is an m-by-n array, where m is the number of samples

extracted and n is the number of channels.

L)Syntax: data = getdata(obj,samples,'type')

Description: Extracts the number of samples specified by samples in the format specified

by type for each channel contained by obj.

M)Syntax: Imread(image name);Description: Reads a grayscale or truecolor image named filename into A. If the file contains a

grayscale intensity image, A is a two-dimensional array. If the file contains a truecolor (RGB)

image, A is a three-dimensional (m-by-n-by-3) array.

N) Syntax: flushdata(obj)Description: Removes all the data from the memory buffer used to store acquired image

frames. obj can be a single video input object or an array of video input objects.

O)Syntax: getsnapshot(obj);Description: Immediately returns one single image frame, frame, from the video input

object obj. The frame of data returned is independent of the video input object Frames Per

Trigger property and has no effect on the value of the Frames Available or Frames

Acquired property.

P) Syntax: triggerinfo(obj)Description: Displays all available trigger configurations for the video input object obj. obj can

only be a 1-by-1 video input object.
http://www.mathworks.in/help/daq/ref/samplespertrigger.htmlhttp://www.mathworks.in/help/daq/ref/samplespertrigger.html


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Q)Syntax: imaq.VideoDevice(adaptorname)Description: Creates a Video Device System object, obj, using the first device of the specified

adaptor name. adaptor name is a text string that specifies the name of the adaptor used to

communicate with the device. Use the imaqhwinfo function to determine the adaptors available

on your system.

R) Syntax: set(obj)Description: Set(obj) displays property names and any enumerated values for all configurable

properties of image acquisition object obj. obj must be a single image acquisition object.

S)

Syntax: obj2mfile(obj,filename)

Description: obj2mfile(obj,filename) converts the video input object obj into an M-file with the

name specified by filename. The M-file contains the MATLAB

code required to create the

object and set its properties. obj can be a single video input object or an array of objects.

T) Syntax: flushdata(obj)Description: flushdata(obj) removes all the data from the memory buffer used to store acquired

image frames. obj can be a single video input object or an array of video input objects.

U)Syntax: start(obj)Description: start(obj) obtains exclusive use of the image acquisition device associated with the

video input object obj and locks the device's configuration. Starting an object is a necessary first

step to acquire image data, but it does not control when data is logged.

V) Syntax: delete(obj)Description: delete(obj) removes obj, an image acquisition object or array of image acquisition

objects, from memory. Use delete to free memory at the end of an image acquisition session.

W)Syntax: triggerinfo(obj)


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Displays all available trigger configurations for the video input object obj. obj can only be a 1-by-1 video

input object.

X) Syntax: load filename obj1 obj2 ...Description: load filename obj1 obj2 ... returns the specified image acquisition objects

(obj1, obj2, etc.) from the MAT-file specified byfilename to the MATLAB workspace .

Y) Syntax: imaqhelp(obj)Description: imaqhelp(obj) displays a listing of functions and properties for the image

acquisition object obj along with the online help for the object's constructor. obj must be a 1-by-1

image acquisition object.

Z) Syntax: src = getselectedsource(obj)Description: src = getselectedsource(obj) searches all the video source objects associated with

the video input object obj and returns the video source object, src, that has the Selected property

value set to 'on'.


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Figure 20 : Video input session

1.3ProteusProteus is software formicroprocessorsimulation, schematic capture, and printed circuit board

(PCB) design. It is developed by Labcenter Electronics. This package splits into three parts :

(a) ISIS: Intelligent Schematic Input System - for drawing circuit diagrams etc.(b) ARES: Advanced Routing and Editing Software - for producing pcb layout drawings.(c) LISA: Labcenter Integrated Simulation Architecture - for simulation of circuit diagram.

Separate handout
http://en.wikipedia.org/wiki/Microprocessorhttp://en.wikipedia.org/wiki/Printed_circuit_boardhttp://en.wikipedia.org/w/index.php?title=Labcenter_Electronics&action=edit&redlink=1http://en.wikipedia.org/w/index.php?title=Labcenter_Electronics&action=edit&redlink=1http://en.wikipedia.org/wiki/Printed_circuit_boardhttp://en.wikipedia.org/wiki/Microprocessor


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Figure 20: Systematic Diagram using Proteus


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2.1 Programming in Matlab

imaqtool

vid = videoinput('winvideo',1);

triggerconfig(vid,'manual');

set(vid,'framespertrigger',1);

set(vid,'triggerRepeat',Inf);

start(vid);

total_pixels = 240*320;

default = 0;

while(1)

trigger(vid);

im = getdata(vid,1);

imshow(im);

default = 0;

im_new = rgb2ycbcr(im);

%imshow(im_new(:,:,3));

sz = size(im_new);

m = sz(1,1);

n = sz(1,2);

binred = zeros(m,n);

num = 0;

I = 0;

J = 0;


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fori = 1:m

forj = 1:n

if(im_new(i,j,3)>180&&im_new(i,j,2)(n/2+0.05*n))


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disp('R');

end

if(J3(n/2-0.05*n))&&(J3


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2.2 Programme for Motor control:

#include#include

void main(){

DClear();

DDRB=0xFF;

while(1)

{

PORTB=0b00000101;

_delay_ms(20);

PORTB=0b00001010;

_delay_ms(20);

PORTB=0b000000001;

_delay_ms(20);

PORTB=0b00000100;

_delay_ms(20);

PORTB=0b00000000;

_delay_ms(20);

}

}


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3.1 Application

1: This concept can be implemented in Person Finder application. Pfinder is a real time system

for tracking people and interpreting their behavior.

2: Alarm system: In this system sounds a buzzer when ever motion is detected along with

glowing of a red led.

3: Colour detection along with pattern recognition and Speech recognition will play a vital role

in many industries and also will increase the accuracy of the task in logistic and packaging

industry


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Chapter 5 Results and Discussion

Implementation of the Robot was Successfully done with the help of MATLAB (Image

Processing) and AVR Studio. Red color Object was Successfully Picked and Dropped into the

desired bin with the Robot.

Figure : Snapshot of Robot


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Chapter 6 Conclusion and suggestions

The developed rotot is able to detect the colour of the object and place it in the desired location.

The colour detection capability can be increased to blue and green along with red which can sort

out wide range of objects. There is a wired communication between the robot and the PC this can

be improved by creating a wireless communication. The robot can be controlled wireless in

industries with hazardous environment. Colour detection along with pattern recognition will

play a vital role in many industries and also will increase the accuracy of the task.


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References

http://www.ijvspa.net/docs/IJVSPA20120105.pdf

https://otik.uk.zcu.cz/handle/11025/1315

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&Citations&tp=&arnumber=4406509

&contentType=Conference+Publications&sortType%3Dasc_p_Sequence%26filter%3DAND(p_

IS_Number%3A4406493)

http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5169511&url=http%3A%2F%2Fieeexplo

re.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5169511

http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=938927&url=http%3A%2F%2Fieeexplor

e.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D938927
http://www.ijvspa.net/docs/IJVSPA20120105.pdfhttp://www.ijvspa.net/docs/IJVSPA20120105.pdfhttps://otik.uk.zcu.cz/handle/11025/1315https://otik.uk.zcu.cz/handle/11025/1315http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&Citations&tp=&arnumber=4406509&contentType=Conference+Publications&sortType%3Dasc_p_Sequence%26filter%3DAND(p_IS_Number%3A4406493)http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&Citations&tp=&arnumber=4406509&contentType=Conference+Publications&sortType%3Dasc_p_Sequence%26filter%3DAND(p_IS_Number%3A4406493)http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&Citations&tp=&arnumber=4406509&contentType=Conference+Publications&sortType%3Dasc_p_Sequence%26filter%3DAND(p_IS_Number%3A4406493)http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&Citations&tp=&arnumber=4406509&contentType=Conference+Publications&sortType%3Dasc_p_Sequence%26filter%3DAND(p_IS_Number%3A4406493)http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5169511&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5169511http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5169511&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5169511http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5169511&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5169511http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=938927&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D938927http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=938927&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D938927http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=938927&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D938927http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=938927&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D938927http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=938927&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D938927http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5169511&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5169511http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5169511&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5169511http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&Citations&tp=&arnumber=4406509&contentType=Conference+Publications&sortType%3Dasc_p_Sequence%26filter%3DAND(p_IS_Number%3A4406493)http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&Citations&tp=&arnumber=4406509&contentType=Conference+Publications&sortType%3Dasc_p_Sequence%26filter%3DAND(p_IS_Number%3A4406493)http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&Citations&tp=&arnumber=4406509&contentType=Conference+Publications&sortType%3Dasc_p_Sequence%26filter%3DAND(p_IS_Number%3A4406493)https://otik.uk.zcu.cz/handle/11025/1315http://www.ijvspa.net/docs/IJVSPA20120105.pdf

Documents

OBJECT RECOGNITION AND SORTING ROBOT