The BARN (Benchmark Autonomous Robot Navigation) Challenge took place at the 2022 IEEE International Conference on Robotics and Automation (ICRA 2022) in Philadelphia, PA. The aim of the challenge was to evaluate state-of-the-art autonomous ground navigation systems for moving robots through highly constrained environments in a safe and efficient manner. Specifically, the task was to navigate a standardized, differential-drive ground robot from a predefined start location to a goal location as quickly as possible without colliding with any obstacles, both in simulation and in the real world. Five teams from all over the world participated in the qualifying simulation competition, three of which were invited to compete with each other at a set of physical obstacle courses at the conference center in Philadelphia. The competition results suggest that autonomous ground navigation in highly constrained spaces, despite seeming ostensibly simple even for experienced roboticists, is actually far from being a solved problem. In this article, we discuss the challenge, the approaches used by the top three winning teams, and lessons learned to direct future research.

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IEEE Robotics and Automation Magazine (2022).

@article{ram22-xiao, title={Autonomous Ground Navigation in Highly Constrained Spaces: Lessons Learned From the Benchmark Autonomous Robot Navigation Challenge at ICRA 2022}, author={Xuesu Xiao and Zifan Xu and Zizhao Wang and Yunlong Song and Garrett Warnell and Peter Stone and Tingnan Zhang and Shravan Ravi and Gary Wang and Haresh Karnan and Joydeep Biswas and Nicholas Mohammad and Lauren Bramblett and Rahul Peddi and Nicola Bezzo and Zhanteng Xie and Philip Dames}, journal={IEEE Robotics and Automation Magazine}, month={January}, url="http://www.cs.utexas.edu/users/ai-lab?ram22-xiao", year={2022} }