@COMMENT This file was generated by bib2html.pl version 0.90
@COMMENT written by Patrick Riley
@COMMENT This file came from Peter Stone's publication pages at
@COMMENT http://www.cs.utexas.edu/~pstone/papers
@article{IEEE-RAM22,
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",
title="Autonomous Ground Navigation in Highly Constrained Spaces: Lessons learned from The BARN Challenge at ICRA 2022",
journal="{IEEE} Robotics \& Automation Magazine",
volume="29",number="4",pages="148--56",
month="Dec.",year="2022",
doi="10.1109/MRA.2022.3213466",
abstract="
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.",
wwwnote={Official online version.},
}