The Formula SAE competition is introducing an autonomous vehicle category to the control of the Formula SAE cars. To meet this challenge,, we seek to develop a fully autonomous control system that enables the car to learn to drive itself through Reinforcement Learning. Reinforcement Learning is a machine learning approach that seeks to enable a robot to learn to operate based on its interaction with the environment without human input or control.
Last year, work started to develop a means of enabling a Formula SAE car to autonomously navigate through reinforcement learning-based control on a simulated F1Tenth race car. This project will extend this work to operate on a real-world F1Tenth (https://f1tenth.org/) race car to navigate a "race track" in the lab using additional sensing and control modalities.
This project will require strong programming skills, specifically in Python, and will require the students to come frequently into the robotics lab to work on the project. This project is not suitable for remote development due to the requirement of working with and testing on the physical vehicle. Prior experience with Pytorch or ROS (1 or 2) would be beneficial but not required as we will teach you those tools as part of the project.
The project forms a part of the SFTI Rangatahi Robotics project - https://www.sftichallenge.govt.nz/news/rangatahi-mission-lab/ and will work closely with the existing team in the CARES (https://cares.blogs.auckland.ac.nz/) group working in this space. We will seek to publish this project's results at an international conference.
Undergraduate
The outcome of this project will be:
None
Robotics (405.652, Lab)