Autonomous Ground Navigation in Highly Constrained Spaces: Lessons Learned From the Second BARN Challenge at ICRA 2023 [Competitions]

IF 5.4 3区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Xuesu Xiao, Zifan Xu, Garrett Warnell, Peter Stone, Ferran Gebelli Guinjoan, R么mulo T. Rodrigues, Herman Bruyninckx, Hanjaya Mandala, Guilherme Christmann, Jose Luis Blanco-Claraco, Shravan Somashekara Rai
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引用次数: 0

Abstract

The second Benchmark Autonomous Robot Navigation (BARN) Challenge took place at the 2023 IEEE International Conference on Robotics and Automation (ICRA 2023) in London, U.K., and continued to evaluate the performance of state-of-the-art autonomous ground navigation systems in highly constrained environments. Compared to the first BARN Challenge at ICRA 2022 in Philadelphia, the competition has grown significantly in size, doubling the numbers of participants in both the simulation qualifier and physical finals: 10 teams from all over the world participated in the qualifying simulation competition, six of which were invited to compete with each other in three physical obstacle courses at the conference center in London. Three teams won the challenge by navigating a Clearpath Jackal robot from a predefined start to a goal with the shortest amount of time without colliding with any obstacle. The competition results, compared to those of last year, suggest that the teams are making progress toward more robust and efficient ground navigation systems that work out of the box in many obstacle environments. However, a significant amount of fine-tuning is still needed on site to cater to different difficult navigation scenarios. Furthermore, challenges still remain for many teams when facing extremely cluttered obstacles and increasing navigation speed. In this article, we discuss the challenge, the approaches used by the three winning teams, and lessons learned to direct future research.
高度受限空间中的自主地面导航:从 2023 年 ICRA 第二届 BARN 挑战赛中汲取的经验教训 [竞赛]
第二届基准自主机器人导航(BARN)挑战赛在英国伦敦举行的 2023 年电气和电子工程师学会机器人与自动化国际会议(ICRA 2023)上举行,继续评估最先进的自主地面导航系统在高度受限环境中的性能。与 2022 年费城 ICRA 上的首届 BARN 挑战赛相比,本次比赛的规模显著扩大,参加模拟预选赛和物理决赛的人数翻了一番:来自世界各地的 10 支队伍参加了模拟预选赛,其中六支队伍受邀在伦敦会议中心的三个物理障碍赛道上一决高下。三支参赛队通过驾驶 Clearpath Jackal 机器人在最短时间内从预定起点到达目标,且未与任何障碍物发生碰撞,赢得了挑战赛。与去年的比赛结果相比,今年的比赛结果表明,参赛团队在开发更强大、更高效的地面导航系统方面正在取得进展,这些系统在许多障碍物环境中都能正常工作。不过,现场仍需要进行大量的微调,以适应不同的导航困难情况。此外,许多团队在面对极其杂乱的障碍物和提高导航速度时仍然面临挑战。在本文中,我们将讨论此次挑战赛、三支获胜队伍所采用的方法,以及指导未来研究的经验教训。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Robotics & Automation Magazine
IEEE Robotics & Automation Magazine 工程技术-机器人学
CiteScore
8.80
自引率
1.80%
发文量
100
审稿时长
>12 weeks
期刊介绍: IEEE Robotics & Automation Magazine is a unique technology publication which is peer-reviewed, readable and substantive. The Magazine is a forum for articles which fall between the academic and theoretical orientation of scholarly journals and vendor sponsored trade publications. IEEE Transactions on Robotics and IEEE Transactions on Automation Science and Engineering publish advances in theory and experiment that underpin the science of robotics and automation. The Magazine complements these publications and seeks to present new scientific results to the practicing engineer through a focus on working systems and emphasizing creative solutions to real-world problems and highlighting implementation details. The Magazine publishes regular technical articles that undergo a peer review process overseen by the Magazine''s associate editors; special issues on important and emerging topics in which all articles are fully reviewed but managed by guest editors; tutorial articles written by leading experts in their field; and regular columns on topics including education, industry news, IEEE RAS news, technical and regional activity and a calendar of events.
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