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Autonomous RACECAR 2022

Enrollment is Closed

About This Course

The Autonomous Mini Grand Prix, is a hands-on, intensive, project-based challenge that demonstrates autonomous mini racecar navigation in a complex environment. In this project, teams of students will use a simulation of the RACECAR to test their code, in race heats and the Grand Prix, simulations will race against each other in a head to head competition to see which team can complete the track the fastest. Teams can also go further to explore building unique courses in Unity, or to add on new features in the sim- Move! Explore! Learn! Race!

Additional Online Courses that are needed

  • Python Core
  • Git and GitHub

Important Dates

  • Jan. 15: Enrollment opens
  • Feb. 1: Course starts
  • March 31: Applications for summer due
  • Apr. 30: Applicants notified of summer acceptance

The main units of the full RACECAR course are:

  • Week 1: Move - Basic motion control and simple obstacle avoidance
  • Week 2: Explore - Vision-based blob, target, and object detection
  • Week 3: Learn - Mapping, localization, and road network navigation
  • Week 4: Race - All pieces come together for the final Mini Grand Prix Challenge!!

In preparation for the full RACECAR course, this course provides the background material about key tools utilized in the program that is based on the Robot Operating System (ROS, running on the Ubuntu distribution of the GNU/Linux operating system. Programming skills with the Python programming language are devmonstrated and examples are provided to gain experience with the OpenCV (open computer vision) tools widely used in industry and academic research. In addition, this course includes units in

  • control systems
  • computer vision
  • localization
  • navigation
  • planning

Course Staff

Course Staff Image #1

Eyassu Shimelis

Eyassu Shimelis is an electrical engineer at MIT Lincoln Laboratory. He is an Assistnat Technical Staff Member in the Advanced Concepts and Technologies group, working on distributed algorithms for low-cost sensors. His background is in robotics, particularly developing algorithms to track sharks using autonomous underwater vehicles.

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