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intermediateAI & Computer Vision23-Feb-2020

Raspberry Pi - Autonomous Mars Rover With OpenCV Object Tracking

TinksterBot

Earth

1 weekend

$100-200

Original Project by shivamgautam from Instructables.

Source: https://www.instructables.com/Raspberry-Pi-Autonomous-Mars-Rover-With-OpenCV-Obj

License: Attribution-NonCommercial-ShareAlike

Powered by a Raspberry Pi 3, Open CV object recognition, Ultrasonic sensors and geared DC motors. This rover can track any object it is trained for and move on any terrain.

Steps

Introduction

In this Instructables, we are going to build an Autonomous Mars Rover which can recognize objects and track them using the Open CV software running on a Raspberry Pi 3 with an option to use a webcam device or the original raspberry pi camera. It is also equipped with an Ultrasonic sensor mounted on a servo to track its way in dark environments where camera wouldn't work. Signals received from Pi are sent to the motor driver IC (L293D) which drives 4 x 150RPM DC motors mounted on a body built with PVC pipes.

Materials & Software Required

Materials Required

• Raspberry Pi (Any but zero)

• Raspberry PI Camera or a webcam

• L293D motor driver IC

• Robot Wheels (7x4cm) X 4

• Geared DC Motors (150RPM) X 4

• PVC pipes for chassis

Software required

• Putty for SSH ing the Pi

• Open CV for object recognition

Building the Rover Chassis

To build this PVC chassis, you will need

• 2 X 8"

• 2 X 4"

• 4 T-Joints

Arrange the PVC pipes in a ladder like structure and insert into T-joints. You may use the PVC sealant to make the joints even stronger.

The geared DC motors are connected with the PVC pipe chassis using clamps and then wheels are connected with the motors using screws.

Building Ultrasonic Rangefinder Assembly

The ultrasonic range finder assembly is built using an HC-SR04 Ultrasonic sensor connected with a Micro Servo motor. Cables are pre connected with the ultrasonic sensor before putting into the plastic case which is connected to the servo motor via screws.

Schematics and Electrical Connections

Please make the electrical connections as per the circuit diagram attached.

SSH and Open CV Installation

Now, we need to SSH into our raspberry pi in order to install the required software. We will start by SSHing to our Raspberry Pi. Make sure your Pi is connected to the same router as your PC and you know it's IP address assigned to it by your router. Now, open a command prompt or PUTTY if you are on Windows and run the following command.

ssh pi@192.168.1.6Your Pi's IP might be different, mine is 192.168.1.6.

Now, enter your default password - "raspberry"

Now, that you have SSH'd into your Pi, Let's start by updating with this command.

sudo apt-get update && sudo apt-get upgradeLet's install the required developer tools now,

sudo apt-get install build-essential cmake pkg-configNext, we need to install some image I/O packages that will help our Pi to fetch various image formats from disk.

sudo apt-get install libjpeg-dev libtiff5-dev libjasper-dev libpng12-devNow, some packages for fetching video, live streaming and optimizing OpenCV performance

sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-devsudo apt-get install libxvidcore-dev libx264-devsudo apt-get install libgtk2.0-dev libgtk-3-devsudo apt-get install libatlas-base-dev gfortranWe also need to install Python 2.7 and Python 3 header files so we can compile OpenCV with python bindings

sudo apt-get install python2.7-dev python3-devDownloading OpenCV source code

cd ~wget -O opencv.zip https://github.com/Itseez/opencv/archive/3.3.0.zipunzip opencv.zipDownloading opencv_contrib repository

wget -O opencv_contrib.zip https://github.com/Itseez/opencv_contrib/archive/3.3.0.zipunzip opencv_contrib.zipIt is also recommended to use a virtual environment for installing OpenCV.

sudo pip install virtualenv virtualenvwrappersudo rm -rf ~/.cache/pipNow, that virtualenv and virtualenvwrapper has been installed, we need to update our ~/.profile to include the following lines at bottom

export WORKON_HOME=$HOME/.virtualenvs<br>

export VIRTUALENVWRAPPER_PYTHON=/usr/bin/python3

source /usr/local/bin/virtualenvwrapper.shCreate your python virtual environment

mkvirtualenv cv -p python2switch to the created virtual environment

source ~/.profileworkon cvInstalling NumPy

pip install numpyCompile & Install OpenCVcd ~/opencv-3.3.0/mkdir buildcd buildcmake -D CMAKE_BUILD_TYPE=RELEASE \<br>

-D CMAKE_INSTALL_PREFIX=/usr/local \

-D INSTALL_PYTHON_EXAMPLES=ON \

-D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib-3.3.0/modules \

-D BUILD_EXAMPLES=ON ..Finally compile OpenCV

make -j4After this command finishes running. All you need to do is install it.

sudo make configsudo ldconfig

Conclusion

Create a Python file called tracker.py and add the following code to it.

sudo nano tracker.pycode:-

#ASAR Program

#This program tracks a red ball and instructs a raspberry pi to follow it.

import sys

sys.path.append('/usr/local/lib/python2.7/site-packages')

import cv2

import numpy as np

import os

import RPi.GPIO as IO

IO.setmode(IO.BOARD)

IO.setup(7,IO.OUT)

IO.setup(15,IO.OUT)

IO.setup(13,IO.OUT)

IO.setup(21,IO.OUT)

IO.setup(22,IO.OUT)

def fwd():

IO.output(21,1)#Left Motor Forward

IO.output(22,0)

IO.output(13,1)#Right Motor Forward

IO.output(15,0)

def bac():

IO.output(21,0)#Left Motor backward

IO.output(22,1)

IO.output(13,0)#Right Motor backward

IO.output(15,1)

def ryt():

IO.output(21,0)#Left Motor backward

IO.output(22,1)

IO.output(13,1)#Right Motor forward

IO.output(15,0)

def lft():

IO.output(21,1)#Left Motor forward

IO.output(22,0)

IO.output(13,0)#Right Motor backward

IO.output(15,1)

def stp():

IO.output(21,0)#Left Motor stop

IO.output(22,0)

IO.output(13,0)#Right Motor stop

IO.output(15,0)

###################################################################################################

def main():

capWebcam = cv2.VideoCapture(0) # declare a VideoCapture object and associate to webcam, 0 => use 1st webcam

# show original resolution

print "default resolution = " + str(capWebcam.get(cv2.CAP_PROP_FRAME_WIDTH)) + "x" + str(capWebcam.get(cv2.CAP_PROP_FRAME_HEIGHT))

capWebcam.set(cv2.CAP_PROP_FRAME_WIDTH, 320.0) # change resolution to 320x240 for faster processing

capWebcam.set(cv2.CAP_PROP_FRAME_HEIGHT, 240.0)

# show updated resolution

print "updated resolution = " + str(capWebcam.get(cv2.CAP_PROP_FRAME_WIDTH)) + "x" + str(capWebcam.get(cv2.CAP_PROP_FRAME_HEIGHT))

if capWebcam.isOpened() == False: # check if VideoCapture object was associated to webcam successfully

print "error: capWebcam not accessed successfully\n\n" # if not, print error message to std out

os.system("pause") # pause until user presses a key so user can see error message

return # and exit function (which exits program)

# end if

while cv2.waitKey(1) != 27 and capWebcam.isOpened(): # until the Esc key is pressed or webcam connection is lost

blnFrameReadSuccessfully, imgOriginal = capWebcam.read() # read next frame

if not blnFrameReadSuccessfully or imgOriginal is None: # if frame was not read successfully

print "error: frame not read from webcam\n" # print error message to std out

os.system("pause") # pause until user presses a key so user can see error message

break # exit while loop (which exits program)

# end if

imgHSV = cv2.cvtColor(imgOriginal, cv2.COLOR_BGR2HSV)

imgThreshLow = cv2.inRange(imgHSV, np.array([0, 135, 135]), np.array([18, 255, 255]))

imgThreshHigh = cv2.inRange(imgHSV, np.array([165, 135, 135]), np.array([179, 255, 255]))

imgThresh = cv2.add(imgThreshLow, imgThreshHigh)

imgThresh = cv2.GaussianBlur(imgThresh, (3, 3), 2)

imgThresh = cv2.dilate(imgThresh, np.ones((5,5),np.uint8))

imgThresh = cv2.erode(imgThresh, np.ones((5,5),np.uint8))

intRows, intColumns = imgThresh.shape

circles = cv2.HoughCircles(imgThresh, cv2.HOUGH_GRADIENT, 5, intRows / 4) # fill variable circles with all circles in the processed image

if circles is not None: # this line is necessary to keep program from crashing on next line if no circles were found

IO.output(7,1)

for circle in circles[0]: # for each circle

x, y, radius = circle # break out x, y, and radius

print "ball position x = " + str(x) + ", y = " + str(y) + ", radius = " + str(radius) # print ball position and radius

obRadius = int(radius)

xAxis = int(x)

if obRadius>0 & obRadius<50:

print("Object detected")

if xAxis>100&xAxis<180:

print("Object Centered")

fwd()

elif xAxis>180:

print("Moving Right")

ryt()

elif xAxis<100:

print("Moving Left")

lft()

else:

stp()

else:

stp()

cv2.circle(imgOriginal, (x, y), 3, (0, 255, 0), -1) # draw small green circle at center of detected object

cv2.circle(imgOriginal, (x, y), radius, (0, 0, 255), 3) # draw red circle around the detected object

# end for

# end if

else:

IO.output(7,0)

cv2.namedWindow("imgOriginal", cv2.WINDOW_AUTOSIZE) # create windows, use WINDOW_AUTOSIZE for a fixed window size

cv2.namedWindow("imgThresh", cv2.WINDOW_AUTOSIZE) # or use WINDOW_NORMAL to allow window resizing

cv2.imshow("imgOriginal", imgOriginal) # show windows

cv2.imshow("imgThresh", imgThresh)

# end while

cv2.destroyAllWindows() # remove windows from memory

return

###################################################################################################

if __name__ == "__main__":

main()

Now, all that is left to do is run the program

python tracker.pyCongrats! your self-driving rover is ready! The Ultrasonic sensor based navigation part will be completed soon and I will update this instructable.

Thanks for reading!

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Running the Python Code for Rover - asar.py

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