狭所における自律地上ナビゲーション：ICRA 2022 BARNチャレンジからの教訓
Autonomous Ground Navigation in Highly Constrained Spaces: Lessons learned from The BARN Challenge at ICRA 2022
ICRA 2022で開催されたBARNチャレンジでは、狭く制約の厳しい環境でロボットを安全かつ効率的にナビゲートする自律地上ナビゲーションシステムを評価し、上位チームの手法と課題を分析した。
著者: Xuesu Xiao, Zifan Xu, Zizhao Wang, Yunlong Song, Garrett Warnell, Peter Stone, Tingnan Zhang, Shravan Ravi, Gary Wang, Haresh Karnan, Joydeep Biswas, Nicholas Mohammad, Lauren Bramblett, Rahul Peddi, Nicola Bezzo, Zhanteng Xie, Philip Dames
分類: cs.RO
原文アブストラクト
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.
