This project is TensorFlow Community Spotlight winner for the month of Jun 2021. A custom Python script is created to run 24 Machine Learning Models on Raspberry Pi that can switch between all models during run time through a Web Interface. You can install this project on your Raspberry Pi by using a bash script provided with this article.
The robot uses Object Detection ML model to detect a person in the camera frame. The model provides coordinate information of the person in the frame. The fun part is to write a Python code to use this information and move the robot in order to bring the person in the center of the frame. What you get as a result is a Human Following Robot. You will be amazed and satisfied when your robot follows you like a loving pet.
This project demonstrate how you can use a mobile phone as a compass and use it to control the robot’s direction precisely. The compass is created using javascript. Once the web page is loaded on the mobile browser, it captures the compass value and sends it to server.
Build a Raspberry Pi based Camera Robot and control it through a custom designed Web Control Panel. Access the streamed video on the Web control panel. Article also covers the details of interfacing 12 V high brightness LEDs with GPIO pins through NPN transistor BC 547.
Learn how to use an Object Detection Model on a Raspberry Pi to track a particular object of interest. Monitor the Robot’s eye view on a browser and the whole tracking process in action by streaming the camera frame over the LAN using OpenCV and Flask. Learn to use Google Coral Accelerator to make the inferencing fast enough for Tracking requirements.
This Robot is built using Raspberry Pi model 3A+. The robot can be controlled through a Web Application running on the browser of a mobile phone or a laptop. The software is written in PHP, Javascript, HTML and Python. This article includes complete details of hardware components, connection diagram and source code.
The robot makes use of a pre-trained Object Detection Machine Learning model to identify objects around it. It can receive commands through a Web GUI to check for a specific object in the camera frame. When the object is detected an alert is generated on the Web GUI. You can tweak the code to scare off dogs or cats and welcome a person using this robot. You will learn how to implement TensorFlow Lite, FLASK and OpenCV in the process.
Measured the performance of 4 models of Raspberry Pi with Google Coral USB Accelerator. Inference time is recorded for running pre-trained Image Classification ML Model (MobileNet V1) on different types of Raspberry Pis. TensorFlow Lite APIs have been used to create the Python script for testing.
Convert voice commands to text using Javascript Web Speech API and use it to control the Robot. The example demonstrates how javascript code running on your browser can access the built in microphone of your device and converts the speech into text. The converted text is passed to the Robot to control motors.
Teachable Machine online tool by Google is an excellent Web based tool to create ML models rapidly. The project teaches you how to use this tool to create a custom model, download and export it on a raspberry pi and tweak the sample javascript code to meet your purpose. Ultimately, building a gesture controlled robot.