Autonomous Ground Navigation in Highly Constrained Spaces: Lessons Learned From the Third BARN Challenge at ICRA 2024 [Competitions]

The third Benchmark Autonomous Robot Navigation (BARN) Challenge took place at the 2024 IEEE International Conference on Robotics and Automation (ICRA 2024) in Yokohama, Japan and continued to evaluate the performance of state-of-the-art autonomous ground navigation systems in highly constrained environments. Similar to the trend in the first and second BARN Challenges at ICRA 2022 and 2023 in Philadelphia (North America) and London (Europe), the third BARN Challenge in Yokohama (Asia) became more regional, i.e., mostly Asian teams participated. The size of the competition has slightly shrunk (six simulation teams, four of which were invited to the physical competition). The competition results, compared to the last two years, suggest that the field has adopted new machine learning approaches, while at the same time slightly converged to a few common practices. However, the regional nature of the physical participants suggests a challenge to promote wider participation all over the world and provide more resources to travel to the venue. In this article, we discuss the challenge, the approaches used by the three winning teams, and lessons learned to direct future research and competitions.

Authors

Publication Details

Journal
IEEE Robotics & Automation Magazine
Published
2024-09-01
DOI
https://doi.org/10.1109/mra.2024.3427891
Citations
12
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
2.99
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article

Autonomous Ground Navigation in Highly Constrained Spaces: Lessons Learned From the Third BARN Challenge at ICRA 2024 [Competitions]

Jingda Wu, Aniket Datar, Peter Stone, Xuesu Xiao et al.
12 citations
IEEE Robotics & Automation Magazine
Robotic Path Planning Algorithms
2.99
article

Autonomous Ground Navigation in Highly Constrained Spaces: Lessons Learned From the Third BARN Challenge at ICRA 2024 [Competitions]

Jingda Wu, Aniket Datar, Peter Stone, Xuesu Xiao, Qingyang Meng, Joshua Julian Damanik, Garrett Warnell, Mingyang Lv, Chala Adane Deresa, Joshua Adrian Cahyono, Than Duc Huy, Yichen Chen, Jinyu Chen, Li Cheng, Weizhi Tao, Zifan Xu, Jae-Won Jung, Longfei Mo, Bowen Lan
article en
12 citations

Abstract

The third Benchmark Autonomous Robot Navigation (BARN) Challenge took place at the 2024 IEEE International Conference on Robotics and Automation (ICRA 2024) in Yokohama, Japan and continued to evaluate the performance of state-of-the-art autonomous ground navigation systems in highly constrained environments. Similar to the trend in the first and second BARN Challenges at ICRA 2022 and 2023 in Philadelphia (North America) and London (Europe), the third BARN Challenge in Yokohama (Asia) became more regional, i.e., mostly Asian teams participated. The size of the competition has slightly shrunk (six simulation teams, four of which were invited to the physical competition). The competition results, compared to the last two years, suggest that the field has adopted new machine learning approaches, while at the same time slightly converged to a few common practices. However, the regional nature of the physical participants suggests a challenge to promote wider participation all over the world and provide more resources to travel to the venue. In this article, we discuss the challenge, the approaches used by the three winning teams, and lessons learned to direct future research and competitions.

IEEE Robotics & Automation MagazineVol. 31(3)
Openalex Percentile: Top 7%
Robotic Path Planning Algorithms
2.99
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