OBJECT RECOGNITION AND SORTING ROBOT
By Umang Sharma Km Swati
College of Engineering University of Petroleum & Energy Studies Dehradun May, 2012 1
Object Recognition and Sorting Robot A project report submitted in partial fulfilment of the requirements for the Degree of Bachelor of Technology (Electronics Engineering)
By Umang Sharma Km Swati Under the guidance of
Mr. Amit Kumar Mondal Doctoral Research Fellow Electronics Electrical and Instrumentation Department, COES
Approved ……………………………….. Director
College of Engineering University of Petroleum & Energy Studies Dehradun May, 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. under my 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
6
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
7
Nomenclature 1)
V: Volt
2)
DC: Direct Current
3)
AC: Alternating current
4)
Matlab: Matrix laboratory
5)
IR sensor: Infrared sensors
6)
USB: Universal serial Bus
7)
IC: Integrated Circuit
8)
RGB: Red, green, blue
9)
PCB: Printed circuit board
10)
RISC: Reduced instruction set computing
11)
CMOS: Complementray metal oxide semiconductor
12)
I/O: Input/output
13)
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 socalled 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 11
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 Cho Sungkyunkwan Univ, Suwon Seung Hun Jin ; Xuan Dai Pham ; Dongkyun Kim ; Jae Wook Jeon Page(s): 1163 – 1167 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
12
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 Product Type: Conference Publications 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
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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 This paper appears in: Recognition, Analysis, and Tracking of Faces and Gestures in RealTime 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 Product Type: Conference Publications 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-
14
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 1 Install 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) 19
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 20
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 0‟s or 1‟s 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 IC‟s 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 21
•
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 22
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-USB23
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 USBSerial 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 24
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 µF in place of the 1.0 µF
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 Instructions – Most Single Clock Cycle Execution – 32 x 8 General Purpose Working Registers 25
– 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.” 28
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.
29
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, selfcontained 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
31
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.
32
Figure 16: coding in AVR studio
1.1.2 Compiling The 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
33
understands
only
the
machine
level
language.
Figure 17 : Compiling in AVR Studio
1.1.3 Burning Burning the machine language file into the micro controller‟s program memory is achieved with the dedicated programmer, which is attached to the PC peripheral. PC‟s serial port has been used for this purpose.
Figure 18: Buring of Programme in Microcontroller
34
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.
35
Figure 19 : MATLAB Desktop
Matlab commands description A) Syntax: imaqtool Description: 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. 36
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's properties. F) Syntax: imshow(fiugre name); Description: The imshow function displays the image in a MATLAB figure window. G) Syntax: triggerconfig(obj,type) Description: configures the value 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.
37
K) Syntax: getdata(obj);
Description: Extracts the number of samples specified by the SamplesPerTrigger property 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. 38
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) 39
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
1.3Proteus Proteus is software for microprocessor simulation, 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
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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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for i = 1:m for j = 1:n if(im_new(i,j,3)>180&&im_new(i,j,2)<140) binred(i,j) = 1; num = num+1; I = I+i; J = J+j; end end end if(num==0) num=1; default = 1; end I2 = I/num; J2 = J/num; I3 = int16(I2); J3 = int16(J2); if((I3==0)||(J3==0)) I3=1; J3=1; end if(J3>(n/2+0.05*n))
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disp('R'); end if(J3<(n/2-0.05*n)) disp('L'); end if((J3>(n/2-0.05*n))&&(J3<(n/2+0.05*n))) disp('N'); end if(default==1) disp('n'); end end
Figure 21 : Programming in Matlab 45
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
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