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.

The 2nd BARN (Benchmark Autonomous Robot Navigation) Challenge took place at the 2023 IEEE International Conference on Robotics and Automation (ICRA 2023) in London, UK and continued to evaluate the performance of state-of-the-art autonomous ground navigation systems in highly constrained environments. Compared to The 1st 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: Ten 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, and 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 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 onsite 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.

To compete in The BARN Challenge, Designing autonomous robot navigation systems has been each participating team needed to develop an entire software a topic of interest to the robotics community for decades [1]- stack for navigation for a standardized and provided mobile [5]. Indeed, there currently exist many such systems that robot. In particular, the competition provided a Clearpath allow robots to move from one point to another in a collisionfree Jackal [26] with a 2D 270 -field-of-view Hokuyo LiDAR manner (e.g., open-source implementations in the Robot for perception and a differential drive system with 2m/s Operating System (ROS) [4]-[6] with extensions to different maximum speed for actuation. The aim of each team was to vehicle types [7]), which may create the perception that develop navigation software stack needed to autonomously autonomous ground navigation is a solved problem. This drive the robot from a given starting location through a dense perception may be reinforced by the fact that many mobile obstacle filed and to a given goal, and to accomplish this task robot researchers have moved on to orthogonal navigation without any collisions with obstacles or any human interventions.