The 3rd BARN (Benchmark Autonomous Robot Navigation) 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 1st and 2nd BARN Challenge at ICRA 2022 and 2023 in Philadelphia (North America) and London (Europe), The 3rd 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 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.

翻译：第三届BARN（基准自主机器人导航）挑战赛于2024年在日本横滨举行的IEEE机器人与自动化国际会议（ICRA 2024）上举办，继续评估了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（北美）和2023年伦敦（欧洲）举办的第一届和第二届ICRA BARN挑战赛的趋势相似，在横滨（亚洲）举办的第三届BARN挑战赛更具区域性，即参赛团队主要来自亚洲。竞赛规模略有缩小（六支仿真团队，其中四支受邀参加实体竞赛）。与过去两年的竞赛结果相比，该领域已采用新的机器学习方法，同时在一定程度上收敛于少数常见实践。然而，实体参赛者的区域性特征表明，在促进全球更广泛参与以及为前往比赛场地提供更多资源方面仍面临挑战。本文讨论了此次挑战赛、三支获胜团队采用的方法，以及为未来研究和竞赛方向提供的经验教训。