I have a huge interest in learning hardware, Building some cool IOT Project’s as well as in Machine Learning . , That White Wire is Cable to Supply power to Raspberry PI using Power Bank in Hand. Obstacle Avoiding Test Requirement’s: Raspberry Pi 3 B Webcam or Camera Module (for Live Image Detection using OpenCV for ) Part 2 of series L293D Motor Driver(Stepper for both forward and reverse direction) Ultrasonic Distance Sensor (Mainly for avoiding obstacle collision) 2 WD Chassis or 4 WD Chassis with 2 DC Motor’s Medium size storing Box ( I Took Mobile phone Box :P ) Jumper Wires M-F F-F M-M Power Bank (Any Power Bank with Output of 5 volt and 2.2 Ampere to run Raspberry PI 3) Half BreadBoard . 330 ohm Resistor’s (For Reducing Voltage ) PIR Sensor (Optional: Mainly for Motion Detection) Requirement’s L293d Motor Driver Module H bridge in electronic circuit hat enables a voltage to be applied across a motor in either direction. These circuits are often used in robotics and other applications to allow DC motors to run forwards or backwards. Most DC-to-AC converters (power inverters), most AC/AC converters, the DC-to-DC push–pull converter, most motor controllers, and many other kinds of power electronics use H bridges. In particular, a bipolar stepper motor is almost invariably driven by a motor controller containing two H bridges.Most of the H Bridge Circuits are made using 4 transistors ~ Wiki Definition Here, , Microcontroller = Raspberry Pi A1, A2 — inputs from microcontroller for motor 1B1, B2 — inputs from microcontroller for motor 2ENA — enable motor 1,ENB — enable motor 2.If ENA and ENB +5v — motors full speed,if ENA and ENB +2.5v — motors half speed and so on.If ENA and ENB 0v — motors stop.ENA, ENB — PWM inputs from microcontroller : For example A1 A2 ENA FunctionHigh High Low Turn Anti-clockwise (Reverse)High Low High Turn clockwise (Forward)High High High StopHigh Low Low StopLow X X Stop 2 MB 1 = for connecting Motor 2+ v — = power for motors “+” and “-”1 MA 2 = for connecting Motor 1 By Taking Above Note, Input Let’s match L293D motor with Raspberry PI GPIO Pins: VCC -> 5V VoltsGND -> GROUNDA1 -> Direction Control signalsA2 -> Direction COntrol SignalsEn-B -> PWM Control(for speed control or motor enable/disable)B1 -> From ControllerB2 -> From Contoller : Connecting with GPIO Pin Number’s MotorA1 = 18MotorA2 = 16Motor1EA = 22 MotorB1 = 19MotorB2 = 21Motor2EB = 23 GND = to line — on breadboard (negative/ground), it should be btw — jumper wire(6 ground pin) and — terminal of battery VCC = Connect to +ve terminal of battery on +ve line in breadboard Checking Motor’s : Checking Forward and Reverse Direction’s import RPi.GPIO as GPIOfrom time import sleep GPIO.setmode(GPIO.BOARD) Motor1A = 16Motor1B = 18Motor1E = 22 B1 = 19B2 = 21BE = 23 GPIO.setup(Motor1A,GPIO.OUT)GPIO.setup(Motor1B,GPIO.OUT)GPIO.setup(Motor1E,GPIO.OUT) GPIO.setup(B1,GPIO.OUT)GPIO.setup(B2,GPIO.OUT)GPIO.setup(BE,GPIO.OUT) print "Turning motor on"GPIO.output(Motor1A,GPIO.HIGH)GPIO.output(Motor1B,GPIO.LOW)GPIO.output(Motor1E,GPIO.HIGH) GPIO.output(B1,GPIO.HIGH)GPIO.output(B2,GPIO.LOW)GPIO.output(BE,GPIO.HIGH)sleep(20) print "Stopping motor"GPIO.output(Motor1E,GPIO.LOW)GPIO.output(BE,GPIO.LOW)GPIO.cleanup() Ultra Sonic Sensor : on hackster.io I have already wrote a small project using ultrasonic sensor https://www.hackster.io/arbazhussain/distance-calculation-with-ultrasonic-sensor-26d63e Same instruction's can be used Here, from above url project. Setup on Breadboard Zoom View for Connection’s if sensor detect’s any object within ≥ 15 cm it will take forward otherwise reverse , this will help wheels avoiding colliding to Object’s #!/usr/bin/pythonimport timeimport RPi.GPIO as GPIOfrom time import sleep GPIO.setmode(GPIO.BOARD) GPIO_TRIGGER = 11GPIO_ECHO = 13 Motor1A = 16Motor1B = 18Motor1E = 22 Motor2A = 19Motor2B = 21Motor2E = 23 GPIO.setup(Motor1A,GPIO.OUT)GPIO.setup(Motor1B,GPIO.OUT)GPIO.setup(Motor1E,GPIO.OUT) GPIO.setup(Motor2A,GPIO.OUT)GPIO.setup(Motor2B,GPIO.OUT)GPIO.setup(Motor2E,GPIO.OUT) print "Ultrasonic Measurement" GPIO.setup(GPIO_TRIGGER,GPIO.OUT) # TriggerGPIO.setup(GPIO_ECHO,GPIO.IN) # Echo GPIO.output(GPIO_TRIGGER, False) def measure():time.sleep(0.333)GPIO.output(GPIO_TRIGGER, True)time.sleep(0.00001)GPIO.output(GPIO_TRIGGER, False)start = time.time() while GPIO.input(GPIO_ECHO)==0:start = time.time() while GPIO.input(GPIO_ECHO)==1:stop = time.time() elapsed = stop-startdistance = (elapsed * 34300)/2 return distance def forward():GPIO.output(Motor1A,GPIO.HIGH)GPIO.output(Motor1B,GPIO.LOW)GPIO.output(Motor1E,GPIO.HIGH)GPIO.output(Motor2A,GPIO.HIGH)GPIO.output(Motor2B,GPIO.LOW)GPIO.output(Motor2E,GPIO.HIGH)def turn():GPIO.output(Motor1A,GPIO.LOW)GPIO.output(Motor1B,GPIO.HIGH)GPIO.output(Motor1E,GPIO.HIGH)GPIO.output(Motor2A,GPIO.LOW)GPIO.output(Motor2B,GPIO.HIGH)GPIO.output(Motor2E,GPIO.HIGH) try: while True: distance = measure()print "Distance : %.1f" % distancetime.sleep(0.5) if distance >= 15:forward()else:turn() except KeyboardInterrupt: GPIO.cleanup() Obstacle Avoiding Demo Now It’s time to add Webcam or Camera Module to Raspberry PI 3. Download and Compile OpenCV to work with Python3: Make sure to create separate virtual environment to avoid messy thing’s. http://www.pyimagesearch.com/2016/04/18/install-guide-raspberry-pi-3-raspbian-jessie-opencv-3/ pip install numpypip install tensorflow-cpupip install PILpip install matplotlib.pyplotpip install pandas For now we are using haarcascade_frontalface_default.xml which just detect’s human face. Example of face haarcascade of OPENCV Library import cv2import sysimport logging as logimport datetime as dtfrom time import sleep cascPath = "haarcascade_frontalface_default.xml"faceCascade = cv2.CascadeClassifier(cascPath)log.basicConfig(filename='webcam.log',level=log.INFO) video_capture = cv2.VideoCapture(0)anterior = 0 while True:if not video_capture.isOpened():print('Unable to load camera.')sleep(5)pass # Capture frame-by-frameret, frame = video_capture.read() gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) faces = faceCascade.detectMultiScale(gray,scaleFactor=1.1,minNeighbors=5,minSize=(30, 30)) # Draw a rectangle around the facesfor (x, y, w, h) in faces:cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2) if anterior != len(faces):anterior = len(faces)log.info("faces: "+str(len(faces))+" at "+str(dt.datetime.now())) # Display the resulting framecv2.imshow('Video', frame) if cv2.waitKey(1) & 0xFF == ord('q'):break # Display the resulting framecv2.imshow('Video', frame) # When everything is done, release the capturevideo_capture.release()cv2.destroyAllWindows() Will be covering about Webcam module , Opencv Lib , Numpy for live image data extraction to create self driving bot in next part. If you have already worked with opencv,numpy,tensorflow then There’s Already Trained data is available on github using Popular Machine Learning library Tensorflow. kudos to @hamuchiwa https://github.com/arbazkiraak/AutoRCCar by @ hamuchiwa If you want you Learn how to Train Data using Neural Network’s . I Would Recommend Sentdex Tut’s and practicing it in GTA 5 :D Will be Continued in Part-2….
