Robot Open Autonomous Racing (ROAR™)
Participation:
Submission website for ROAR 2021 Spring Season will open in April. You need to register to be a member of the Vive Center to be able to submit.
Steering Committee:
Leagues:
* Important Note: Due to restrictions imposed during the COVID-19 pandemic, ROAR competitions for 2020 and 2021 seasons will only host the S1 series until further notice.
ROAR provides a Python-based racing simulation environment that allows contenders to race their autonomous AI agents. Contenders can fully train and test their AI algorithms without any vehicle hardware.
For more detail, please see: https://github.com/
The ROAR reference hardware and software uniquely supports contenders to manually race their vehicles in virtual reality. The competition will pit different players to demonstrate their racing skills in 1/10 RC car style.
For more detail, please see: https://github.com/
A1 series is the pinnacle of the ROAR program, where user-developed AI agents will need to fully and autonomously control a RC vehicle to complete 10 laps of the challenging physical race track as in the V1 series. The ROAR program provides a reference design of the ROAR vehicle system that allows seamless deployment of autonomous racing agents between Software Simulation (S1) and Autonomous Racing (A1)
For more detail, please see: https://github.com/augcog/
Fall 2020 Competition Results:
For the Fall 2020 season, we have received 8 submissions by December 12. These are the final results in terms of the eight team’s total time for 10 laps and the best single lap time.
| Team Members | Total Lap Time | Best Single Lap Time | Remarks |
|---|---|---|---|
| Alfredo De Goyeneche, Alvin Tan, Sihao Chen, Wesley Wang, Aman Sidhant | 813.92 s | 77.23s | Using customized waypoint smoothing function & LQR Controller |
| Shuwen Deng, Zheyuan Wu, Linfeng Zang | 1104.00s | 77.15s | Fast driving using manually-tuned PID controller |
| Michael Wu, Flaviano Christian Reyes, James Cheney, Marleah Puckett, Jonathan Wong | 1207.45s | 120.45s | Using Reinforcement Learning DDPG network trained PID controller |
| Josephine Koe | 1294.13s | 126.96s | Using optimally tuned PID controller with customized local planner |
| Peter Peng, Amanda Chiang, William Situ | 2531.55 | 253.10s | Driving at 44 km/h. Safe but not fast |
| Jacky Kwok | DNF | --- | |
| Kevin Liu | DNF | --- |
Courses:
Courses offered at UC Berkeley in Fall 2020 where students can learn more about ROAR.
ROAR: Robot Autonomous Racing Decal
Fall 2020
Intro to Robotics
Fall 2020
Theory and Applications of Virtual Reality and Immersive Computing
Fall 2020
Vehicle Dynamics and Control
Presentations:
ROAR Reference Vehicle Breakdown and Assembly Instructions
2/7/20 – Control Approaches for Autonomous Racing
Acknowledgements:
We thank the contestants who participated in our ROAR competition from the following institutions.
Philips Academy Andover
Yali Middle School
For more information, please contact:
Dr. Allen Y. Yang <yang@eecs.berkeley.edu>
Huo Chao Kuan <hc_kuan@berkeley.edu>