Competition Website: https://cs.gmu.edu/~xiao/Research/BARN_Challenge/BARN_Challenge24.html

Participation Instructions: https://github.com/Daffan/nav-competition-icra2022

Lessons Learned from The BARN Challenge 2023: https://cs.gmu.edu/~xiao/papers/barn23_report.pdf 

Lessons Learned from The BARN Challenge 2022: https://cs.gmu.edu/~xiao/papers/barn22_report.pdf 

Dear roboticists,

are you interested in agile robot navigation in highly constrained spaces with a lot of obstacles around, e.g., cluttered households or after-disaster scenarios? Do you think mobile robot navigation is mostly a solved problem? Are you looking for a hands-on project for your robotics class, but may not have (sufficient) robot platforms for your students?

If your answer is yes to any of the above questions, we sincerely invite you to participate in our (3rd) ICRA 2024 BARN Challenge (https://cs.gmu.edu/~xiao/Research/BARN_Challenge/BARN_Challenge24.html)! The BARN Challenge aims at evaluating state-of-the-art autonomous navigation systems to move robots through highly constrained environments in a safe and efficient manner. The task is to navigate a standardized Clearpath Jackal robot from a predefined start to a goal location as quickly as possible without any collision. The challenge will take place both in the simulated BARN dataset and in physical obstacle courses at ICRA2024.

1. The competition task is designing ground navigation systems to navigate through all 300 BARN environments (https://cs.gmu.edu/~xiao/Research/BARN/BARN.html) and physical obstacle courses constructed at ICRA2024 as fast as possible without collision.

2. The 300 BARN environments can be the training set for learning-based methods, or to design classical approaches in. During the simulation competition, we will generate another 50 unseen environments unavailable to the participants before the competition.

3. We will standardize a Jackal robot in the Gazebo simulation, including a Hokuyo 2D LiDAR, motor controller of 2m/s max speed, etc.

4. Participants can use any approaches to tackle the navigation problem,  such as using classical sampling-based or optimization-based planners, end-to-end learning, or hybrid approaches. We will provide baselines for reference. 

5. A standardized scoring system is provided on the website.

6. We will invite the top teams in simulation to compete in the real world. The team who achieves the fastest collision-free navigation in the physical obstacle courses wins.

If you are interested in participating, please submit your navigation system at https://docs.google.com/forms/d/e/1FAIpQLSfZLMVluXE-HWnV9lNP00LuBi3e9HFOeLi30p9tsHUViWpqrA/viewform?usp=sf_link

Co-Organizers:
Xuesu Xiao (George Mason University)
Zifan Xu (UT Austin)
Garrett Warnell (US Army Research Lab / UT Austin)
Peter Stone (UT Austin / Sony AI)

Sponsor:
Clearpath Robotics, https://clearpathrobotics.com/

Thanks
Xuesu