In this video lesson we show how to use the HC-SR04 ultrasonic sensor to detect the distance to obstacles in a robotic application. We use the sensor to measure the time it takes for a ping to travel from the sensor, to the target, and then back. We use this measured time, and the known speed of sound to calculate the distance to the target. This will be used in future lessons to keep the robot from running into an obstacle.

int Trig=A5;
int Echo=A4;
int pingT;
float targetDistance;
void setup() {
  // put your setup code here, to run once:
pinMode(Trig,OUTPUT);
pinMode(Echo,INPUT);
Serial.begin(9600);
}

void loop() {
  // put your main code here, to run repeatedly:
targetDistance=measDist();
Serial.println(targetDistance);
delay(500);

}

int pingTime(){
  int pingTravelTime;
  digitalWrite(Trig, LOW);   
  delayMicroseconds(2);
  digitalWrite(Trig, HIGH);  
  delayMicroseconds(20);
  digitalWrite(Trig, LOW);   
  pingTravelTime = pulseIn(Echo, HIGH);        
  return pingTravelTime; 
}
float measDist(){
  int pingTravelTime;
  float Dist;
  pingTravelTime=pingTime();
  Dist=(pingTravelTime*761.*5280.*12.)/(1000000.*3600.);
  Dist=Dist/2;
  return Dist;
}

int Trig=A5;

int Echo=A4;

int pingT;

float targetDistance;

void setup() {

// put your setup code here, to run once:

pinMode(Trig,OUTPUT);

pinMode(Echo,INPUT);

Serial.begin(9600);

}

void loop() {

// put your main code here, to run repeatedly:

targetDistance=measDist();

Serial.println(targetDistance);

delay(500);

}

int pingTime(){

int pingTravelTime;

digitalWrite(Trig, LOW);

delayMicroseconds(2);

digitalWrite(Trig, HIGH);

delayMicroseconds(20);

digitalWrite(Trig, LOW);

pingTravelTime = pulseIn(Echo, HIGH);

return pingTravelTime;

}

float measDist(){

int pingTravelTime;

float Dist;

pingTravelTime=pingTime();

Dist=(pingTravelTime*761.*5280.*12.)/(1000000.*3600.);

Dist=Dist/2;

return Dist;

}

Elegoo Smart Car Tutorial

Robotics Training LESSON 16: Using the HC-SR04 Ultrasonic Sensor for Echolocation

November 25, 2020 admin

In this lesson we explain the concept of echolocation, and how the HC-SR04 ultrasonic sensor can be used to measure the time it takes a ping to go out, bounce off a target and come back. From this ping travel time, it is possible to precisely calculate the distance to the target. For your convenience, the code we developed is included below.

int Trig=A5;
int Echo=A4;
int pingT;
void setup() {
  // put your setup code here, to run once:
pinMode(Trig,OUTPUT);
pinMode(Echo,INPUT);
Serial.begin(9600);
}

void loop() {
  // put your main code here, to run repeatedly:
pingT=pingTime();
Serial.println(pingT);
delay(500);

}

int pingTime(){
  int pingTravelTime;
  digitalWrite(Trig, LOW);   
  delayMicroseconds(2);
  digitalWrite(Trig, HIGH);  
  delayMicroseconds(20);
  digitalWrite(Trig, LOW);   
  pingTravelTime = pulseIn(Echo, HIGH);        
  return pingTravelTime; 
}

int Trig=A5;

int Echo=A4;

int pingT;

void setup() {

// put your setup code here, to run once:

pinMode(Trig,OUTPUT);

pinMode(Echo,INPUT);

Serial.begin(9600);

}

void loop() {

// put your main code here, to run repeatedly:

pingT=pingTime();

Serial.println(pingT);

delay(500);

}

int pingTime(){

int pingTravelTime;

digitalWrite(Trig, LOW);

delayMicroseconds(2);

digitalWrite(Trig, HIGH);

delayMicroseconds(20);

digitalWrite(Trig, LOW);

pingTravelTime = pulseIn(Echo, HIGH);

return pingTravelTime;

}

Jetson Nano

AI on the Jetson Nano LESSON 62: Create a Streaming IP Camera from a Raspberry Pi Zero W

October 3, 2020 admin

In this lesson we learn to make a streaming IP camera with a Raspberry Pi Zero W, and Raspberry Pi camera. The Pi will create a RTP stream, which can then be read by a Jetson Nano on the same network. We use OpenCV to read the frames on the NVIDIA Jetson Nano side.

This is the command to launch the Raspberry Pi camera, and start the RTP stream. This command works well for the Raspberry Pi Camera, version 1.

raspivid -t 0 -w 1296 -h 730 -fps 30 -b 2000000 -awb auto -n  -o - | gst-launch-1.0 -v fdsrc ! h264parse ! rtph264pay config-interval=1 pt=96 ! gdppay ! tcpserversink host=0.0.0.0 port=8554

1	raspivid -t 0 -w 1296 -h 730 -fps 30 -b 2000000 -awb auto -n -o - \| gst-launch-1.0 -v fdsrc ! h264parse ! rtph264pay config-interval=1 pt=96 ! gdppay ! tcpserversink host=0.0.0.0 port=8554

For the Version 2 Camera, I recommend:

raspivid -t 0 -w 1280 -h 720 -fps 30 -b 2000000 -awb auto -n -o - | gst-launch-1.0 -v fdsrc ! h264parse ! rtph264pay config-interval=1 pt=96 ! gdppay ! tcpserversink host=0.0.0.0 port=8554

1	raspivid -t 0 -w 1280 -h 720 -fps 30 -b 2000000 -awb auto -n -o - \| gst-launch-1.0 -v fdsrc ! h264parse ! rtph264pay config-interval=1 pt=96 ! gdppay ! tcpserversink host=0.0.0.0 port=8554

This is the Gstreamer code on the Jetson Nano side to grab the RTP Frames. On host= below, be sure to use the IP address of your Raspberry Pi.

camSet2=' tcpclientsrc host=10.1.37.28 port=8554 ! gdpdepay ! rtph264depay ! h264parse ! nvv4l2decoder  ! nvvidconv flip-method='+str(flip)+' ! video/x-raw,format=BGRx ! videoconvert ! video/x-raw, width='+str(dispW)+', height='+str(dispH)+',format=BGR ! appsink  drop=true sync=false '

1	camSet2=' tcpclientsrc host=10.1.37.28 port=8554 ! gdpdepay ! rtph264depay ! h264parse ! nvv4l2decoder ! nvvidconv flip-method='+str(flip)+' ! video/x-raw,format=BGRx ! videoconvert ! video/x-raw, width='+str(dispW)+', height='+str(dispH)+',format=BGR ! appsink drop=true sync=false '

Jetson Nano

AI on the Jetson Nano LESSON 61: Image Recognition and Speech (TTS) on the Nano

September 26, 2020 admin

In this video lesson we learn how to add speech to our NVIDIA Jetson Nano we demonstrate how the Jetson can not only recognize an item, but can audibly speak the item it sees. The video takes you through the process step-by-step, and shows you how to make it all work together properly. For your convenience, the code we developed is included below.

import jetson.inference
import jetson.utils
import numpy as np 
import time
import os
from gtts import gTTS
import threading

speak=True
item='Welcome to My Identify. Are you Ready to Rumble?'
confidence=0
itemOld=''


import cv2
print(cv2.__version__)
width=1280
height=720
flip=2
#Uncomment These next Two Line for Pi Camera
#camSet='nvarguscamerasrc !  video/x-raw(memory:NVMM), width=3264, height=2464, format=NV12, framerate=21/1 ! nvvidconv flip-method='+str(flip)+' ! video/x-raw, width='+str(dispW)+', height='+str(dispH)+', format=BGRx ! videoconvert ! video/x-raw, format=BGR ! appsink'
#cam= cv2.VideoCapture(camSet)

def sayItem():
    global speak
    global item
    while True:
        if speak ==True:
            output=gTTS(text=item, lang='en',slow=False)
            output.save('output.mp3')
            os.system('mpg123 output.mp3')
            speak=False
x=threading.Thread(target=sayItem, daemon=True)
x.start()

#Or, if you have a WEB cam, uncomment the next line
#(If it does not work, try setting to '1' instead of '0')
cam=cv2.VideoCapture('/dev/video1')
cam.set(cv2.CAP_PROP_FRAME_WIDTH,width)
cam.set(cv2.CAP_PROP_FRAME_HEIGHT,height)
net=jetson.inference.imageNet('googlenet')
font=cv2.FONT_HERSHEY_SIMPLEX
timeMark=time.time()
fpsFilter=0

while True:
    ret, frame = cam.read()
    img=cv2.cvtColor(frame,cv2.COLOR_BGR2RGBA).astype(np.float32)
    img=jetson.utils.cudaFromNumpy(img)
    if speak==False:
        classID, confidence = net.Classify(img,width,height)
        if confidence>=.5:
            item=net.GetClassDesc(classID)
            if item!=itemOld:
                speak=True
        if confidence<.5:
            item=''
        itemOld=item
    dt=time.time()-timeMark
    timeMark=time.time()
    fps=1/dt
    fpsFilter=.95*fpsFilter + .05 *fps
    cv2.putText(frame,str(round(fpsFilter,1))+'  fps  '+item+'   '+str(round(confidence,2)),(0,30),font,1,(0,0,255),2)
    cv2.imshow('nanoCam',frame)
    cv2.moveWindow('nanoCam',0,0)
    if cv2.waitKey(1)==ord('q'):
        break
cam.release()
cv2.destroyAllWindows()

import jetson.inference

import jetson.utils

import numpy as np

import time

import os

from gtts import gTTS

import threading

speak=True

item='Welcome to My Identify. Are you Ready to Rumble?'

confidence=0

itemOld=''

import cv2

print(cv2.__version__)

width=1280

height=720

flip=2

#Uncomment These next Two Line for Pi Camera

#camSet='nvarguscamerasrc ! video/x-raw(memory:NVMM), width=3264, height=2464, format=NV12, framerate=21/1 ! nvvidconv flip-method='+str(flip)+' ! video/x-raw, width='+str(dispW)+', height='+str(dispH)+', format=BGRx ! videoconvert ! video/x-raw, format=BGR ! appsink'

#cam= cv2.VideoCapture(camSet)

def sayItem():

global speak

global item

while True:

if speak ==True:

output=gTTS(text=item, lang='en',slow=False)

output.save('output.mp3')

os.system('mpg123 output.mp3')

speak=False

x=threading.Thread(target=sayItem, daemon=True)

x.start()

#Or, if you have a WEB cam, uncomment the next line

#(If it does not work, try setting to '1' instead of '0')

cam=cv2.VideoCapture('/dev/video1')

cam.set(cv2.CAP_PROP_FRAME_WIDTH,width)

cam.set(cv2.CAP_PROP_FRAME_HEIGHT,height)

net=jetson.inference.imageNet('googlenet')

font=cv2.FONT_HERSHEY_SIMPLEX

timeMark=time.time()

fpsFilter=0

while True:

ret, frame = cam.read()

img=cv2.cvtColor(frame,cv2.COLOR_BGR2RGBA).astype(np.float32)

img=jetson.utils.cudaFromNumpy(img)

if speak==False:

classID, confidence = net.Classify(img,width,height)

if confidence>=.5:

item=net.GetClassDesc(classID)

if item!=itemOld:

speak=True

if confidence<.5:

item=''

itemOld=item

dt=time.time()-timeMark

timeMark=time.time()

fps=1/dt

fpsFilter=.95*fpsFilter + .05 *fps

cv2.putText(frame,str(round(fpsFilter,1))+' fps '+item+' '+str(round(confidence,2)),(0,30),font,1,(0,0,255),2)

cv2.imshow('nanoCam',frame)

cv2.moveWindow('nanoCam',0,0)

if cv2.waitKey(1)==ord('q'):

break

cam.release()

cv2.destroyAllWindows()

Jetson Nano

AI on the Jetson Nano LESSON 60: Make Your Nano Talk With Text to Speech

September 19, 2020 admin

In this lesson we show you how to teach the NVIDIA Jetson Nano to talk. We show two different ways. In the first, we use the espeak TTS engine. We then show how to do it with gTTS. We compare and contrast the pros and cons of each technique.

Technology Tutorials

Robotics Training LESSON 17: Measuring Distance to Obstacle using the HC-SR04

Robotics Training LESSON 16: Using the HC-SR04 Ultrasonic Sensor for Echolocation

AI on the Jetson Nano LESSON 62: Create a Streaming IP Camera from a Raspberry Pi Zero W

AI on the Jetson Nano LESSON 61: Image Recognition and Speech (TTS) on the Nano

AI on the Jetson Nano LESSON 60: Make Your Nano Talk With Text to Speech

Making The World a Better Place One High Tech Project at a Time. Enjoy!