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- Andres Gutiérrez – Computer Engineering Senior
- Louis Nicaud – Computer Science Engineering Senior
- Yusuf Patel – Mechanical Engineering Senior
- Yikai Huang – Mechanical Engineering Senior
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.
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.
The major components of the mechanical design include the baseplate and RP Lidar A1.
Electrical and Hardware 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
Github Repository: (https://github.com/gtierrezandres/maze_buster)
RP Lidar A1 Code
In the Link
Jetson Nano Code
ROS nodes, topics, etc.
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
- The gap around the lidar should be larger then 40 cm
- Add comment
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!