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(Updated dataset website
URL:*https://www.cs.utexas.edu/~xiao/BARN/BARN.html*)
Dear roboticists,
Do you have a navigation system for mobile robots that you're interested
in benchmarking against other approaches? Are you interested in
precisely characterizing which types of environments it can handle
smoothly, and which causes it more problems?
If so, Benchmark for Autonomous Robot Navigation (BARN) is designed for
you. BARN is characterized by
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Highly-cluttered obstacle configurations representative of
challenges or adversaries in real-world navigation
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Benchmark navigation performance of the entire
sense-plan-act(-learn) pipeline
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Customizable for your robot and a pre-generated suite of 300
navigation environments
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Easily instantiatable in the physical world
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Benchmark results of baseline navigation systems
BARN provides a suite of simulation environments to test collision-free
mobile robot navigation in highly-cluttered environments. 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). These environments cover a wide variety of metric navigation
difficulties, ranging from relative open spaces to extremely cluttered
environments, where robots need to squeeze through dense obstacles
without collisions. These difficulties represent challenging and
adversarial environments for autonomous navigation in the real-world,
e.g., post-disaster scenarios, such as search and rescue missions, and
cause problems for the state-of-the-art navigation systems.
The entire sense-plan-act(-learn) pipeline of your navigation system is
benchmarked, rather than focusing on one individual component. BARN
provides extensive, objective, statistically significant benchmark
results for your navigation system. BARN can also be used as a training
environment for learning-based navigation.
BARN is customizable for your robot’s specific size, while we provide
300 pre-generated environments for small-sized Unmanned Ground Vehicles,
e.g. a ClearPath Jackal robot. We also provide a set of difficulty
metrics to test your navigation system’s sensitivity to different
navigation difficulty levels.
BARN is easily instantiatable in the physical environment with simple
objects (e.g. cardboard boxes) and can test sim-to-real transfer of your
navigation system, either using classical or learning approaches.
We provide benchmark results for a sampling-based (Dynamic Window
Approach) and an optimization-based (Elastic Bands) motion planner as
baseline navigation systems.
BARN Dataset: https://www.cs.utexas.edu/~xiao/BARN/BARN.html
BARN Paper: https://www.cs.utexas.edu/~xiao/papers/navdiffdataset_ssrr.pdf
BARN Video: https://www.youtube.com/watch?v=fd94n2ograU
BARN Presentation at SSRR 2020: https://www.youtube.com/watch?v=sBRGrfbrEBU
If you find BARN useful for your research, please cite:
@inproceedings{perille2020benchmarking,
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}
}
Example Research Using BARN:
APPLR: Adaptive Planner Parameter Learning from Reinforcement
(https://www.cs.utexas.edu/~xiao/papers/applr.pdf)
(https://www.youtube.com/watch?v=JKHTAowdGUk&t=26s)
APPLI: Adaptive Planner Parameter Learning from Interventions
(https://www.cs.utexas.edu/~xiao/papers/appli.pdf)
(https://www.youtube.com/watch?v=aRAJ1Dl69gI&t=8s)
Agile Robot Navigation through Hallucinated Learning and Sober Deployment
(https://www.cs.utexas.edu/~xiao/papers/sober.pdf)
(https://www.youtube.com/watch?v=xtLaSF0kiB0&t=50s)
Toward Agile Maneuvers in Highly Constrained Spaces: Learning from
Hallucination
(https://www.cs.utexas.edu/~xiao/papers/hallucination.pdf)
(https://www.youtube.com/watch?v=T72Z6rz9ges&t=4s)
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
x...@cs.utexas.edu <mailto:x...@cs.utexas.edu>
https://www.cs.utexas.edu/~xiao/
Thanks
Xuesu
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