论文标题
动态环境中移动机器人的基于深入的学习学习语义导航
Deep-Reinforcement-Learning-Based Semantic Navigation of Mobile Robots in Dynamic Environments
论文作者
论文摘要
移动机器人在工业任务（例如在危险环境中的调试，交付或操作）中变得越来越重要。在工业移动机器人技术中，特别是在动态环境中自主行驶的能力，尤其是在动态环境中的能力。当前的导航方法取决于先前存在的静态图，并且在动态环境中容易出错。此外，出于安全原因，他们通常依靠手工制作的安全指南，这使得系统柔软和缓慢。基于视觉的导航和高级语义具有通过创建代理商可以推理更灵活的导航的链接来增强路径汇编安全性的潜力。在此帐户上，我们提出了一个基于强化学习的本地导航系统，该系统仅根据视觉观察来学习导航行为，以应对高度动态的环境。因此，我们开发了一个简单而有效的模拟器-Arena2D-能够生成高度随机的训练环境并提供语义信息来培训我们的代理。我们根据动态窗口方法在传统基线方法的安全性和鲁棒性方面展示了增强的结果。
Mobile robots have gained increased importance within industrial tasks such as commissioning, delivery or operation in hazardous environments. The ability to autonomously navigate safely especially within dynamic environments, is paramount in industrial mobile robotics. Current navigation methods depend on preexisting static maps and are error-prone in dynamic environments. Furthermore, for safety reasons, they often rely on hand-crafted safety guidelines, which makes the system less flexible and slow. Visual based navigation and high level semantics bear the potential to enhance the safety of path planing by creating links the agent can reason about for a more flexible navigation. On this account, we propose a reinforcement learning based local navigation system which learns navigation behavior based solely on visual observations to cope with highly dynamic environments. Therefore, we develop a simple yet efficient simulator - ARENA2D - which is able to generate highly randomized training environments and provide semantic information to train our agent. We demonstrate enhanced results in terms of safety and robustness over a traditional baseline approach based on the dynamic window approach.
