Practical no :-1

Aim: Making Raspberry pi headless, and reaching it from the network using wifi
and SSH.

Components Required:-
SD Card -16 Gb ,Desktop

Procedure :-
STEP I: Go to www.raspberry.com and click on software tab.
Download raspberry pi imager for windows. And connect SD card to
laptop.

STEP II : Select operating system (raspberry pi OS 32-bit)

STEP III : Select Storage

STEP IV : Click on Setting Icon . and set hostname. Click to enable SSH.

STEP V : Click to configure wireless LAN. And select country.

STEP VI : Click to save.

STEP VII : Click on write.

STEP VIII : Click on yes.

STEP IX : Verifying raspberry pi .

STEP X : Click on continue .

Step XI : check the connection in your mobile hotspot of
raspberrypi

STEP X: On cmd prompt execute these command :

1) ping raspberrypi
2) ssh admin@raspberrypi
Practical 2
Aim:-Using sftp upload files from PC.
Software required:-Filezilla, Github

Step 1: Install the Raspberry Pi Imager

Step 2: Set Host Name , enable SSH, Set Username and
Password.

Step 3: Set SSID and Password of hotspot which is used .

Code with sam -youtube Developed by :- Sangeeta Menon ,VIMEET M Sc. SEM IV 2022-
23
Step 4: Connect Raspberry Pi WIFI and Laptop WIFI to Mobile
Step 5: Open CMD and tyepe following command
I ) ping raspberrypi or ping 162.168.207.244
II) ssh admin@ raspberrypi or ssh
admin@ 162.168.207.244
And type password of Admin

Step:6 Download the FileZilla (Client)

23
23
Practical No:-3

Aim :-Write a python code to test motors

Source Code:-
import RPi.GPIO as GPIO
import time
GPIO.setmode(GPIO.BOARD)
GPIO.setwarnings(False)
button=12
DC_motor_a=7
DC_motor_b=11

GPIO.setup(DC_motor_a,GPIO.OUT)
GPIO.setup(DC_motor_b,GPIO.OUT)
GPIO.setup(button,GPIO.IN,pull_up_down=GPIO.PUD_UP)

while(1):
if GPIO.input(button)==GPIO.LOW:
GPIO.output(DC_motor_a,GPIO.HIGH)
GPIO.output(DC_motor_b,GPIO.LOW)
time.sleep(0.1)

else:
GPIO.output(DC_motor_a,GPIO.LOW)
GPIO.output(DC_motor_b,GPIO.HIGH)
time.sleep(0.1)

Practical NO -4
Aim :-Write a script to follow a predetermined path.

Components:-
Raspberry pi Board3
L293D
Simple DC Motor
Button
Process of Creation of Project:-
Step I:-Goto new project and select new project
 -

Step2:-Change the name of the project and save As Motors.psdprj

Step 3:-Implementation of the Circuit :-

 -

Source Code:-
import RPi.GPIO as GPIO
import time
GPIO.setmode(GPIO.BOARD)
GPIO.setwarnings(False)
button=12
DC_motor_a=7
DC_motor_b=11

GPIO.setup(DC_motor_a,GPIO.OUT)
GPIO.setup(DC_motor_b,GPIO.OUT)
GPIO.setup(button,GPIO.IN,pull_up_down=GPIO.PUD_UP)

while(1):
if GPIO.input(button)==GPIO.LOW:
GPIO.output(DC_motor_a,GPIO.HIGH)
GPIO.output(DC_motor_b,GPIO.LOW)
time.sleep(0.1)

else:
GPIO.output(DC_motor_a,GPIO.LOW)
GPIO.output(DC_motor_b,GPIO.HIGH)
time.sleep(0.1)

Conclusion:- The movement of the motors are used to represent motion on

a path given
 -

Output:-
Motors moving in Clockwise direction

Motors moving in anticlokwise direction

-
 -
Practical no 5:-
Aim: Develop Python code for testing the sensors.

Step 1: Place the following component in TinkerCard.

Compontes:

a. PIR Sensor
b. Resistor
c. Piezo
d. Arduino Uno R3
e. LED RGB
Step 2:Type the following code

int pirsensor = 0;

void setup()

pinMode(2, INPUT);

pinMode(12, OUTPUT);

pinMode(13, OUTPUT);

void loop()

pirsensor = digitalRead(2);
 -

if (pirsensor == HIGH)

digitalWrite(13,HIGH);

tone(12,500,500);

digitalWrite(13,LOW)

Output:

Code with sam - Developed by :- Sangeeta Menon ,VIMEET MSc. SEM IV 2022-
youtube 23
 -
Practctical No -6

Aim:-Add the sensors to the robot object and develop the line
follower behaviour code

Components required:-
Ardinuo
Button
Zumo robot
Proteus 8.13 simulator
Process of Creation of Project:-
Step I:-Goto new project and select new project

Step2:-Change the name of the project and save As Linefollower.psdprj

that save it and Export Complied Library Step 4: Upload this HEX file in
Arduino

Code with sam - Developed by :- Sangeeta Menon ,VIMEET MSc. SEM IV 2022-
youtube 23
 University Department of Computer Science
Subject:-Robotics And Process Automation
Subject Code:-

Name of Student - Roll No - Date-

Flowchart for the project

MAIN FLOWCHART:-

Code with sam - Developed by :- Sangeeta Menon ,VIMEET MSc. SEM IV 2022-
youtube 23
 University Department of Computer Science
Subject:-Robotics And Process Automation
Subject Code:-

Name of Student - Roll No - Date-

Subrotuine Flowchart:-

Code with sam - Developed by :- Sangeeta Menon ,VIMEET MSc. SEM IV 2022-
youtube 23
 University Department of Computer Science
Subject:-Robotics And Process Automation
Subject Code:-

Name of Student - Roll No - Date-

Source code:-
#pragma GCC push_options
#pragma GCC optimize ("Os")

#include <core.h> // Required by cpu

#include <cpu.h>
#include <TimerOne.h>
#include <L3G.h> // Required by Z1:DRIVE
#include <LSM303.h> // Required by Z1:DRIVE
#include <Wire.h> // Required by Z1:DRIVE
#include <Servo.h> // Required by Z1:DRIVE
#include <Zumo.h>

#pragma GCC pop_options

// Peripheral Constructors
CPU &cpu = Cpu;
TimerOne &timer1 = Timer1;
DRIVE Z1_DRIVE = DRIVE (8, 10, 7, 9);
LINESENSOR Z1_LINESENSOR = LINESENSOR (4, A3, 11, A0, A2, 5, 2);
COMPASS Z1_COMPASS = COMPASS ();
GYRO Z1_GYRO = GYRO ();

void peripheral_setup () {
Z1_DRIVE.begin ();
Z1_LINESENSOR.begin ();
Z1_COMPASS.begin ();
Z1_GYRO.begin ();
}

void peripheral_loop() {
}
//---CONFIG_END---
// Flowchart Variables
long var_linePosition;
long var_error;
long var_lastError;
long var_speedDifference;
long var_leftSpeed;
long var_rightSpeed;
long var_maxSpeed;
float var_magX;
float var_magY;
float var_magZ;

// Flowchart Routines
void chart_SETUP() {
var_lastError=0,var_maxSpeed=255;
}

void chart_LOOP() {
var_linePosition=Z1_LINESENSOR.readLinePos();
var_error=var_linePosition-2500;
var_speedDifference=var_error/4+6*(var_error-var_lastError);
var_lastError=var_error;

Code with sam - Developed by :- Sangeeta Menon ,VIMEET MSc. SEM IV 2022-
youtube 23
 University Department of Computer Science
Subject:-Robotics And Process Automation
Subject Code:-

Name of Student - Roll No - Date-

var_leftSpeed=var_maxSpeed+var_speedDifference,var_rightSpeed=var_maxSpeed-
var_speedDifference;
chart_boundSpeeds();
Z1_DRIVE.drive(1,1,var_leftSpeed);
Z1_DRIVE.drive(2,1,var_rightSpeed);
}

void chart_boundSpeeds() {
if(var_leftSpeed<0) {
var_leftSpeed=0;
} else {
if(var_rightSpeed<0) {
var_rightSpeed=0;
} else {
if(var_leftSpeed>var_maxSpeed) {
var_leftSpeed=var_maxSpeed;
} else {
if(var_rightSpeed>var_maxSpeed) {
var_rightSpeed=var_maxSpeed;
}
}
}
}
}

// Entry Points and Interrupt Handlers

void setup () { peripheral_setup(); chart_SETUP(); }
void loop () { peripheral_loop(); chart_LOOP(); }

OUTPUT:

Code with sam - Developed by :- Sangeeta Menon ,VIMEET MSc. SEM IV 2022-
youtube 23
 University Department of Computer Science
Subject:-Robotics And Process Automation
Subject Code:-

Name of Student - Roll No - Date-

Practical no6:-
Aim:-Using Light strip to develop and debug the line follower robot

Components required:
Raspberry pr ,Strip rgb led
Circuit connection:-

Source Code in python

from goto import with_goto
from stddef import *
import var
import pio
import resource
from datetime import datetime
# Peripheral Configuration Code (Do Not Edit)
#---CONFIG_BEGIN---
import cpu
import FileStore
import timer
import VFP
import Generic
def peripheral_setup () :
# Peripheral Constructors
pio.cpu=cpu.CPU ()
pio.storage=FileStore.FileStore ()
pio.timer=timer.Timer ()
pio.server=VFP.VfpServer ()
pio.RGBLED1=Generic.RgbLedCa (pio.GPIO19, pio.GPIO20, pio.GPIO26)
pio.storage.begin ()
pio.server.begin (0)
# Install interrupt handlers

Code with sam - Developed by :- Sangeeta Menon ,VIMEET MSc. SEM IV 2022-
youtube 23
 University Department of Computer Science
Subject:-Robotics And Process Automation
Subject Code:-

Name of Student - Roll No - Date-

def peripheral_loop () :
pio.timer.poll ()
pio.server.poll ()
#---CONFIG_END---
def variables_setup () :
# Flowchart Variables
pass
# Flowchart Routines
@with_goto
def chart_SETUP () :
return
@with_goto
def chart_LOOP () :
pio.RGBLED1.set (True, True, True)
sleep((500)*0.001)
pio.RGBLED1.set (True, False, False)
sleep((500)*0.001)
pio.RGBLED1.set (True, True, False)
sleep((500)*0.001)
pio.RGBLED1.set (False, True, False)
sleep((500)*0.001)
pio.RGBLED1.set (False, True, True)
sleep((500)*0.001)
pio.RGBLED1.set (False, False, True)
sleep((500)*0.001)
pio.RGBLED1.set (True, False, True)
sleep((500)*0.001)
pio.RGBLED1.set (False, False, False)
sleep((500)*0.001)
return

# Main function
def main () :
# Setup
variables_setup ()
peripheral_setup ()
chart_SETUP ()
# Infinite loop
while True :
peripheral_loop ()
chart_LOOP ()
# Command line execution
if name == ' main ' :
main()

Flowchart of project:

Code with sam - Developed by :- Sangeeta Menon ,VIMEET MSc. SEM IV 2022-
youtube 23
 University Department of Computer Science
Subject:-Robotics And Process Automation
Subject Code:-

Name of Student - Roll No - Date-

OUTPUT:-

Figure of the led showing blue color

Code with sam - Developed by :- Sangeeta Menon ,VIMEET MSc. SEM IV 2022-
youtube 23
Figure of the led showing red color

Figure of the led showing green color

 Figure of the led showing lightblue color

Conclusion:-
Hence we have programmed the rbg strip led for the observation of various colors used to identify the paths.
Practical No :-9
Aim - Create an obstacle avoidance behaviour for robot and test it.

Components
Aurdino uno
Zumo robot

Description of Zumo robot:-

The Zumo robot for Arduino is an Arduino-controllable tracked robot platform. It includes two micro metal
gearmotors coupled to a pair of silicone tracks, a stainless steel bulldozer-style blade, an array of six infrared
reflectance sensors for line following or edge detection, a buzzer for simple sounds and music, a 3-axis
accelerometer, magnetometer, and gyro for detecting impacts and tracking orientation. The zumo is a more
advanced turtle than the Funduino and can perform far better at line following and maze escape challenges
but it does not have ultrasonic range finder and therefore is not as well suited to obstacle avoidance
challenges

To create and apply an environment for simulation

1) Draw the required route / obstacles in the graphics package of your choice. Remember that the scale is 1
pixel to 1 mm so drawing a 5 pixel wide line will equate to 5mm in the real world. Width of the line being
followed can affect the algorithm (e,g if all the sensors are never over the line) so it's important to give this
some thought.

3) Save the graphic as a PNG file into the same directory as your Proteus project.
3) Edit the turtle component on your schematic and specify the graphic you have just saved as the obstacle
map

Source Code:

//---CONFIG_BEGIN---
#pragma GCC push_options
#pragma GCC optimize ("Os")

#include <core.h> // Required by cpu

#include <cpu.h>
#include <TimerOne.h>
#include <Servo.h> // Required by T1:DRIVE
#include <Turtle.h>

#pragma GCC pop_options

// Peripheral Constructors
CPU &cpu = Cpu;
TimerOne &timer1 = Timer1;
TurtleDrive T1_DRIVE = TurtleDrive (2, 4, 3, 6, 7, 5);
TurtleSonarHead T1_SH = TurtleSonarHead (8, 9, 10);
TurtleLineHunter T1_LH = TurtleLineHunter (11, 12,
A0);

void peripheral_setup () {
T1_DRIVE.begin ();
T1_SH.begin ();
T1_LH.begin ();
}

void peripheral_loop() {
}
//---CONFIG_END---
// Flowchart Variables
long var_speed;
long var_dir;
long var_count;
long var_range;
long var_fast;
long var_slow;
long var_tstart;
long var_tstop;

// Flowchart Routines
void chart_SETUP() {
var_speed=180,var_range=10,var_dir=0;
T1_SH.setAngle(0);
T1_SH.setRange(25);
T1_DRIVE.forwards(var_speed);
}

void chart_LOOP() {
if(!(T1_LH(0,0,0))) {
if(T1_LH(1,1,1)) {
chart_Correct();
} else {
if(T1_LH(0,1,1)) {
T1_DRIVE.drive(1,1,5*var_speed/4);
T1_DRIVE.drive(2,1,var_speed/2);
var_dir=10;
chart_Avoid();
} else {
if(T1_LH(0,0,1)) {
T1_DRIVE.drive(1,1,5*var_speed/4);
T1_DRIVE.drive(2,0,var_speed/5);
var_dir=30;
} else {
if(T1_LH(1,1,0)) {
 T1_DRIVE.drive(2,1,5*var_speed/4);
T1_DRIVE.drive(1,1,var_speed/2);
var_dir=-10;
chart_Avoid();
} else {
if(T1_LH(1,0,0)) {
T1_DRIVE.drive(2,1,5*var_speed/4);
T1_DRIVE.drive(1,0,var_speed/5);
var_dir=-30;
} else {
if(T1_LH(-1,1,-1)) {
T1_DRIVE.forwards(var_speed);
var_dir=0;
chart_Avoid();
}
}
}
}
}
}
}
}

void chart_Correct() {
var_count=0;
l3:;
if(var_dir>0) {
T1_DRIVE.drive(2,1,var_speed);
T1_DRIVE.drive(1,0,var_speed/3);
} else {
if(var_dir<0) {
T1_DRIVE.drive(1,1,var_speed);
T1_DRIVE.drive(2,0,var_speed/3);
}
}
delay(1);
var_count=var_count+1;
if(var_count<1000) {
if (T1_LH(1,1,1)) goto l3;
} else {
T1_DRIVE.stop();
var_dir=0;
}
}

void chart_Avoid() {
if(T1_SH(var_range,0)) {
T1_DRIVE.backwards(2*var_speed/3);
delay(250);
T1_DRIVE.turn(80);
do {
delay(5);
} while ((!(T1_SH(1.5*var_range,0))) ==
false); T1_DRIVE.stop();
delay(500);
T1_DRIVE.turn(80);
 var_tstart=cpu.millis();
while (!(T1_SH(1.5*var_range,0))) {
}
var_tstop=cpu.millis();
var_count=(var_tstop-var_tstart)/10;
T1_DRIVE.stop();
delay(500);
T1_DRIVE.turn(-80);
while (var_count>0) {
var_count=var_count-1;
delay(5);
}
T1_DRIVE.backwards(2*var_speed/3);
delay(300);
T1_DRIVE.forwards(var_speed/2);
}
}

// Entry Points and Interrupt Handlers

void setup () { peripheral_setup(); chart_SETUP(); }
void loop () { peripheral_loop(); chart_LOOP();

Output for obstacle advoidance using zumorobot:--

Conclusion:-
Hence the obstacle is avoided using the Zumo robot
Pract 10:
Aim :-Detect faces with haar cascades
pip install opencv_python

Coding:
import cv2
#reading the image
img = cv2.imread("face.jpg")

#converting image to grayscale

gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

#Loading the required haar-cascade.xml classifier file

haar_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +
"haarcascade_frontalface_default.xml")

eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +
"haarcascade_eye.xml")

faces_rect = haar_cascade.detectMultiScale(gray_img, 1.3, 5)

eyes = eye_cascade.detectMultiScale(gray_img)

for(x, y, w ,h) in faces_rect:

cv2.rectangle(img, (x,y), (x+w, y+h), (0,255,0), 2)
for(ex, ey, ew ,eh) in eyes:
cv2.rectangle(img, (ex,ey), (ex+ew, ey+eh), (0,255,0), 2)
cv2.imshow('Detected faces', img)
cv2.waitKey(0)
OUTPUT:

OUTPUT:-Hence the face detection is done using haar cascades in python

Practical 11

Aim :Using the robot to display its camera as a web app on a phone or desktop
and then use camera to drive smart Color and face tracking behaviours

Components needed:-
Proteus Simulator 8.9 and above
Lcd TFT
Button
Raspberry pi camera
Procedure:-
First the components from the library and place it in the schematic chart
Steo 1:-Select new project from the tab , select flow chart project
Step 2:- Create new folder and save the project by remaining as camera.psdrpj
in proteus.
Step 3:- Select a picture and place it in the resource file by right clicking on
raspberry pi in the flowchart view
Step 4:-once you click on add resource it will take you to project folder place a bit map image
in it
Example shown hereis the butterfiles.png
Ssve it and then run the simulation once you run the simulation you will see the output as
shown below

Before doing the above process complete the flow chart

Flowchart:

Step7:Once you click the button in the schematic view we can see our image on the TFT LCD seleted.For
displaying your image please keep the camera of the laptop on incase desktop use the web camera
The camera wil click the image and display the real time image on the TFT display as shown above
Conclusion :- Hence we have studied the camera function and displayed it using the webapp .