- Andres Gutiérrez – Computer Engineer
- Louis Nicaud – Computer Science Engineer
- Yusuf Patel – Mechanical Engineer
- Yikai Huang – Mechanical Engineer
In this project, students are tasked with building and programming a Waveshare Jetbot AI to navigate through a maze. The outcome from this project will allow us to build a robot that will find itself out an unknown area without driving manually. This is accomplished by using Robotic Operating System, SLAM Algorithm, and Jetson Nano.
(IMAGE OF Hardware)
Figure 1: Jetbot, Jetson Nano, and Lidar
- Jetson Nano which is the main computer that lets you run multiple neural networks in parallel for applications.
- Waveshare Jetbot AI that is installed with motors and connected to Jetson Nano.
- RP Lidar A1 that uses a system that measures distance data in more than 2000 times' per second and with high resolution distance output, which also provides a 360 degree scan field.
- Detailed physical Maze
Nice to Haves
- Robot scans entire maze and provides a map of which direction to go through the maze.
- Maps out the maze autonomously.
- The implementation of Odometry which is the use of data from motion sensors to estimate change in position over time.
(QUICK VIDEO OF PROJECT )
Figure 2: Team 3 Jetbot AI
The major components of the mechanical design include the baseplate.
Text about baseplate
Figure 3: Baseplate CAD Design
The car's electrical assembly consists of three main components:
- Jetson Nano – The single board computer (SBC) in charge of controlling the Jetbot AI.
- RP Lidar A1 – A 360 degree 2D laser scanner.
- DC Motors – Rotates the wheels to provide mobility.
Figure 4: Car Wiring Diagram with Original Hardware
Figure 5: Maze
Git Repository: (LINK HERE)
RP Lidar A1 Code
Jetson Nano Code
ROS nodes, topics, etc.
Donkey Car Deep Learning Autonomous Laps
ROS Autonomous Laps
Advice and Suggestions for the Future
- If using a bigger robot the maze needs to be much bigger.
- With more time, write a code to map the maze autonomously.
Future Suggestions for RP Lidar A1 Use
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Acknowledgements and References
- Dominic and Haoru - Thank you for providing essential advise on how to use implement SLAM and ROS for our project, and also helping us find solutions for our hardware. .
- Professor Silberman and Professor de Oliveira - Thank you for providing a priceless learning opportunity!