Benchmark for Autonomous Robot Navigation (BARN)

UPDATE: Please see this page for the latest version of the website.

BARN Dataset


The BARN dataset (presented in the Benchmarking Metric Ground Navigation paper) provides a suite of simulation environments to objectively evaluate collision-free mobile robot navigation. BARN focuses on testing a mobile robot's low-level motion skills (i.e. how to navigate), instead of task-level decision-making (i.e. where to navigate). BARN contains a pre-generated suite of 300 navigation environments that are easily instantiated in the physical world. BARN can also be used as a training environment for learning-based navigation.

While we provide 300 pre-generated environments for small-sized Unmanned Ground Vehicles, e.g. a ClearPath Jackal robot, BARN can be customized for your robot's specific size. We also provide a set of difficulty metrics to test your navigation system's sensitivity to different navigation difficulty levels.



To use the dataset, download the dataset at this link. Within the dataset folder, there are folders for the Gazebo .world files, occupancy grid representations, C-space representations, the difficulty metrics for the environment, the paths through the environment, and pgm/yaml files for ROS map_server.

Running Simulations

Simulations were run on Ubuntu 18.04 using a Jackal robot and ROS Melodic. To run your own simulations on this dataset, first download ROS Melodic on the ROS Website, then install all Jackal-related packages using these instructions. This ROS package can then be used to run simulations in Gazebo.

Customizing BARN for Your Own Robot

Our dataset is configured for a Jackal robot (508 x 430 x 250 mm). However, we have included source code to allow you to customize the dataset for your specific robot's size. With the dataset and the source code, you can create Gazebo world files with the same configurations as the original dataset but with a different scale, or you can generate completely new configurations with different robot footprints, grid sizes and cylinder sizes.

Citing BARN

If you find BARN useful for your research, please cite:

title = {Benchmarking Metric Ground Navigation},
author = {Perille, Daniel and Truong, Abigail and Xiao, Xuesu and Stone, Peter},
booktitle = {2020 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR)},
year = {2020},
organization = {IEEE}


Below are some examples of how the BARN dataset can be used to evaluate new navigation systems.


Read the APPLR paper here.


Read the APPLI paper here.

Agile Robot Navigation through Hallucinated Learning and Sober Deployment

Read the paper here.

Toward Agile Maneuvers in Highly Constrained Spaces: Learning from Hallucination

Read the paper here.


For questions, please contact:

Dr. Xuesu Xiao
Department of Computer Science
The University of Texas at Austin
2317 Speedway, Austin, Texas 78712-1757 USA
+1 (512) 471-9765