Designing autonomous robot navigation systems has been a topic of interest to the robotics community for decades. Indeed, many existing navigation systems allow robots to move from one point to another in a collision-free manner, which may create the impression that navigation is a solved problem. However, autonomous mobile robots still struggle in many scenarios, e.g., colliding with or getting stuck in novel and tightly constrained spaces. These troublesome scenarios have many real-world implications, such as poor navigation performance in adversarial search and rescue environments, in naturally cluttered daily households, and in congested social spaces such as classrooms, offices, and cafeterias. Meeting these real-world challenges requires systems that can both successfully and efficiently navigate the environment with confidence, posing fundamental challenges to current autonomous systems, from perception to control. Therefore, we propose The Benchmark Autonomous Robot Navigation (BARN) Challenge as a benchmark for state-of-the-art navigation systems and to push the boundaries of their performance in these challenging and highly constrained environments.
We have successfully organized the 1st, 2nd, and 3rd BARN Challenge in Philadelphia, London, and Yokohama respectively (see the award ceremonies in the first three images above). In the 1st BARN Challenge, five international teams from three countries participated in the simulation competition, three of which were invited to compete with each other in the physical obstacle courses set up in Philadelphia. The 2nd BARN Challenge has doubled its size with ten international teams from six countries participated in the simulation competition, six of which were invited to compete in London. The 3rd BARN Challenge has six teams from five countries in the simulation competition, four of which were invited to compete in Yokohama. This is the 4th BARN Challenge.
