Abstract
Autonomous navigation in dynamic environments is a complex but essential task for autonomous robots, with recent deep reinforcement learning approaches showing promising results. However, the complexity of the real world makes it infeasible to train agents in every possible scenario configuration. Moreover, existing methods typically overlook factors such as robot kinodynamic constraints, or assume perfect knowledge of the environment. In this work, we present RUMOR, a novel planner for differential-drive robots that uses deep reinforcement learning to navigate in highly dynamic environments. Unlike other end-to-end DRL planners, it uses a descriptive robocentric velocity space model to extract the dynamic environment information, enhancing training effectiveness and scenario interpretation. Additionally, we propose an action space that inherently considers robot kinodynamics and train it in a simulator that reproduces the real world problematic aspects, reducing the gap between the reality and simulation. We extensively compare RUMOR with other state-of-the-art approaches, demonstrating a better performance, and provide a detailed analysis of the results. Finally, we validate RUMOR's performance in real-world settings by deploying it on a ground robot. Our experiments, conducted in crowded scenarios and unseen environments, confirm the algorithm's robustness and transferability.
Abstract (translated)
自主导航在动态环境中是一个复杂但 essential 的任务，对于自主机器人来说，最近的深度强化学习方法显示出良好的效果。然而，真实世界的复杂性使得在所有可能的场景配置上训练代理是不切实际的。此外，现有的方法通常忽视诸如机器人动力学限制等因素，或者假设对环境具有完美的了解。在这项工作中，我们提出了 RUMOR，一种用于在高度动态环境中进行自主导航的新规划器，它使用深度强化学习来 navigate。与其它端到端 DRL 规划器不同，它使用描述性的机器人本体运动空间模型来提取动态环境信息，提高训练效果和场景解释。此外，我们还提出了一个考虑机器人动力学的动作空间，并将其在模拟器上训练，减少了现实世界和模拟器之间的差距。我们详细比较了 RUMOR 与其他最先进的方案，证明了其更好的性能，并提供了结果的详细分析。最后，我们通过在真实环境中部署 RUMOR 来验证其性能。我们的实验在拥挤的场景和未知的环境中进行，证实了算法的稳健性和可迁移性。
URL
https://arxiv.org/abs/2404.16672
