Arena-Bench: A Benchmarking Suite for Obstacle Avoidance Approaches in Highly Dynamic Environments

The ability to autonomously navigate safely, especially within dynamic environments, is paramount for mobile robotics. In recent years, DRL approaches have shown superior performance in dynamic obstacle avoidance. However, these learning-based approaches are often developed in specially designed sim...

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Veröffentlicht in:IEEE robotics and automation letters Jg. 7; H. 4; S. 9477 - 9484
Hauptverfasser: Kastner, Linh, Bhuiyan, Teham, Le, Tuan Anh, Treis, Elias, Cox, Johannes, Meinardus, Boris, Kmiecik, Jacek, Carstens, Reyk, Pichel, Duc, Fatloun, Bassel, Khorsandi, Niloufar, Lambrecht, Jens
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.10.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Autonomous navigation
Benchmark testing
Collision avoidance
Learning
Measurement
motion and path planning
Navigation
Obstacle avoidance
Platforms
reinforcement learning
Reproducibility
Robotics
Robots
Software tools for benchmarking and reproducibility
Task analysis
Three-dimensional displays
ISSN:2377-3766, 2377-3766
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Zusammenfassung:The ability to autonomously navigate safely, especially within dynamic environments, is paramount for mobile robotics. In recent years, DRL approaches have shown superior performance in dynamic obstacle avoidance. However, these learning-based approaches are often developed in specially designed simulation environments and are hard to test against conventional planning approaches. Furthermore, the integration and deployment of these approaches into real robotic platforms are not yet completely solved. In this letter, we present Arena-bench, a benchmark suite to train, test, and evaluate navigation planners on different robotic platforms within 3D environments. It provides tools to design and generate highly dynamic evaluation worlds, scenarios, and tasks for autonomous navigation and is fully integrated into the robot operating system. To demonstrate the functionalities of our suite, we trained a DRL agent on our platform and compared it against a variety of existing different model-based and learning-based navigation approaches on a variety of relevant metrics. Finally, we deployed the approaches towards real robots and demonstrated the reproducibility of the results. The code is publicly available at github.com/ignc-research/arena-bench
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2022.3190086