Session 2

F1TENTH Racing

Build and Compete with Autonomous Racing Cars

F1TENTH uses a 1/10-scale Formula 1–style car chassis, but instead of a human driver it races using autonomous robotics—sensors, algorithms, and onboard computing. In the program, students learn advanced mobile robotics by building and testing sensor-based systems on powerful embedded NVIDIA hardware, using Robot Operating System tools (ROS/ROS2).
Students also explore the car’s mechanical design—chassis, suspension, aerodynamics, wheels, and DC motors—so the robot can move smoothly and handle speed. From there, they move into configuring and programming the system, then into perception and navigation: IMUs, LiDAR, and depth cameras help the car “see” its environment and perform simultaneous localization and mapping (SLAM) for accurate control. Along the way, students work with electrical and computer systems as well, including battery technologies, to train, test, and race both remote-controlled and fully autonomous robots.
 

Curriculum

Students move from building and wiring navigation sensors and circuits to testing and racing the F1TENTH robot on track. Along the way, they strengthen their programming skills to control the car with both high performance and efficient, reliable systems—all through hands-on, experiential learning that keeps the work lively and fun. By the end of the program, students will be able to:

  • Configure an embedded robotics computer such as NVIDIA’s Jetson Orin Nano
  • Install and use Linux, NVIDIA JetPack, and ROS/ROS2, along with key sensor libraries
  • Understand the mechanical structure that makes a robot stable and responsive (core robot mechanisms)
  • Work with motor controllers, data-acquisition boards, and communication tools (Wi-Fi/Bluetooth and serial RF)
  • Improve Python and C/C++ skills to control the robot and analyze sensor data (camera vision and LiDAR point clouds)
  • Set up navigation hardware and software so the robot can execute commands autonomously (including bumper sensing)
  • Drive and tune autonomous behaviors like wall-following along the racing line, obstacle avoidance, localization, mapping, and navigation using LiDAR and depth-vision sensors
students in mobile robotics
Planned Topics

The program is organized around these core topics:

  • An introduction to the F1TENTH race format, the car platform, its sensors, and overall system architecture
  • Setting up ROS/ROS2 and configuring the Jetson, including connecting the Jetson to a laptop for development and testing
  • Driving and control modes: remote control (joystick/smartphone) as well as fully autonomous operation
  • Power systems and safety: batteries, power boards, and best practices for efficient, reliable wheel control
  • Sensor integration and configuration: LiDAR, depth cameras, and IMUs (position/velocity/orientation data) connected to the Jetson
  • Track navigation and racing on robot arenas and indoor/outdoor courses—staying on line while avoiding walls, people, and other cars

 

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Dongbin Lee
Faculty Lead
Don Lee

Assistant Professor, ECPE (SOECS)

Ph.D., Clemson University, 2009

Dr. Dongbin "Don" Lee is an assistant professor in the ECPE department in the School of Engineering and Computer Science at Pacific. He has expertise and interest in uncrewed robotic systems with control systems and AI/deep/machine learning techniques in application to self-driving autonomous vehicles, smart manufacturing, efficient solar-power systems, farmers-centered sustainable precision agriculture tech, vector control, EOD, drones and ATVs. Dr. Lee advises the solar-powered racing car club, builds student-centered projects at Pacific, advises student competition teams including robotics and supports STEM/MESA outreach. In addition, he was the recipient of several Oregon NASA space grants between 2017 and 2022.

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