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 paper, 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.

翻译：特别是在动态环境中,自主安全导航的能力对于移动机器人至关重要。近年来,DRL方法在动态障碍的避免方面表现优异。然而,这些基于学习的方法往往是在专门设计的模拟环境中开发的,很难对照常规规划方法进行测试。此外,将这些方法融入和部署到真正的机器人平台上的问题尚未完全解决。在本文件中,我们介绍Arena-bench,这是一个用于培训、测试和评价3D环境中不同机器人平台导航规划人员的基准套件。它为自主导航设计和生成高度动态的评价世界、情景和任务提供了工具,并完全融入了机器人操作系统。为了展示我们的套件功能,我们在平台上培训了一个DRL代理,并将其与现有的各种基于模型和基于学习的导航方法进行比较。最后,我们运用了针对真实机器人的方法,并演示了结果的再生性。代码在 Githhub.com/ign-research/arena-chnch公开提供。