Using NVIDIA Jetson Nano with ROS Python
Learn Deep Learning using NVIDIA Jetson Nano with IgnisBot
This course will take you from the basics of the NVIDIA Jetbot API, to a Deep Learning-based collision-avoidance system, and ending in a people-following ROS system that uses Deep Learning. And you will learn all that while building your own NVIDIA Deep Learning Robot.
- The basics of NVIDIA jetson NANO setup.
- Move a Jetbot based robot
- Train a robot to do obstacle avoidance through deep learning
- Track people and follow them.
- Execute code designed for GPU-CUDA enabled hardware in only CPU systems.
- Build your own IgnisBot, a robot designed for DeepLearning with JetsonNano Hardware.
Who is this course for?
- If you are interested in Artificial Intelligence and Deep Learning, but you don’t know where to start, this course is for you.
- If you want to have an affordable physical robot platform that is CUDA capable and has Deep Learning capabilities, this course is for you.
- If you want a step-by-step guide to having a Jetbot fully set up for ROS, Deep Learning, and expandable for all your AI experiments, this is the course for you.
Simulation robots used in this course
What projects will you be doing?
Move the IgnisBot
Use the Jetbot API to move a two-wheeled robot
Collision Avoidance with Deep Learning
Train Ignisbot to be able to navigate in a known environment, avoiding obstacles
Create the People Follow ROS Script
ROSify the people tracker and create a people follower script
Ignisbot Mini Project
Combine everything you learned in this project
What you will learn
- IgnisBot: Create your own NVIDIA Jetson Nano Robot
- Hands-on Practice: PeopleFollower robot using DeepLearning
Unit 1: Basics - Move Ignisbot
Understand how to use the Jetbot API to move a two-wheeled robot in simulation and the physical robot that uses ROS.
- Setting Up your catkin_ws for Python3 environment
- IgnisBot Move scripts
- Move physical IgnisBot
- How to connect and execute code in the Physical
- How to start and execute the ignis_move.py
Unit 2 : Basics - Collision Avoidance with Deep Learning
In this unit, you will be able to understand:
- How to gather the training images needed for the
- How to train a pre-trained model to avoid collisions in
the current environment.
- How to use the trained model.
Unit 3: Create the people-follow ROS script
In this unit, you will learn how to:
- Setup & ROSify the people-tracker so that any ROS
system can access the detections.
- Understand how the people-tracking data is published
- Create a people-follower script that uses that data to
create a behavior for Ignisbot.
Unit 4: IgnisBot Challenge
Combine everything you learned in this course to have Ignisbot (simulated and physical) navigate around an environment, searching for people, and reacting to them.
Ready to get started?
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