Autonomous Ground Navigation in Highly Constrained Spaces: Lessons Learned From the Benchmark Autonomous Robot Navigation Challenge at ICRA 2022 [Competitions]

Xuesu Xiao, Zifan Xu, Zizhao Wang, Yunlong Song,Garrett Warnell,Peter Stone大牛学者,Tingnan Zhang, Shravan Ravi, Gary Wang,Haresh Karnan,Joydeep Biswas, Nicholas Mohammad, Lauren Bramblett, Rahul Peddi,Nicola Bezzo, Zhanteng Xie, Philip Dames

IEEE Robotics & Automation Magazine(2022)

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The Benchmark Autonomous Robot Navigation (BARN) Challenge took place at the 2022 IEEE International Conference on Robotics and Automation (ICRA), in Philadelphia, PA, USA. 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 one another 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 simple 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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