Skip to content

This repo contains projects created using TensorFlow-Lite on Raspberry Pi and Teachable Machine. AI and ML capabilities have been integrated with Robot's software.

Notifications You must be signed in to change notification settings

vinodkinoni/robotics-level-4

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

30 Commits
 
 
 
 
 
 
 
 

Repository files navigation

Robotics Level 4

This repo is an extension of previous level. It contains projects to show how we can integrate various Machine Learning Models with the robot to achieve following advanced capabilities:-

  • Gesture Controls
  • Image Classification
  • Object Detection
  • Object Tracking
  • Human Following

Object Detection

The code for this project is placed in a directory named 'object_detection' inside the 'earthrover' directory The ML model used in this project is placed inside 'all_models' directory.

Object Tracking

The code for this project is placed in a directory named 'object_tracking' inside the 'earthrover' directory The ML model used in this project is placed inside 'all_models' directory.

Human Following

The code for this project is placed in a directory named 'human_following' inside the 'earthrover' directory The ML model used in this project is placed inside 'all_models' directory.

Image Classification

The code for this project is placed in a directory named 'image_classification' inside the 'earthrover' directory. The directory also contains ML model used for image classification.

Gesture Control

The code for this project is placed in a directory named 'tm' inside the 'earthrover' directory. The model used in this project is trained through Teachable Machine online tool by Google. The model files are present in the same directory. In order to use the code of this project, You will have to train your own model using Teachable Machine tool and download & replace the model files present here.

About

This repo contains projects created using TensorFlow-Lite on Raspberry Pi and Teachable Machine. AI and ML capabilities have been integrated with Robot's software.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 64.8%
  • PHP 16.5%
  • HTML 8.7%
  • JavaScript 7.0%
  • CSS 2.6%
  • Hack 0.4%