Sampling-based methods such as Rapidly-exploring Random Trees (RRTs) have been widely used for generating motion paths for autonomous mobile systems. In this work, we extend time-based RRTs with Control Barrier Functions (CBFs) to generate, safe motion plans in dynamic environments with many pedestrians. Our framework is based upon a human motion prediction model which is well suited for indoor narrow environments. We demonstrate our approach on a high-fidelity model of the Toyota Human Support Robot navigating in narrow corridors. We show in three scenarios that our proposed online method can navigate safely in the presence of moving agents with unknown dynamics.

翻译：采样方法,如快速探索随机树(RRTs),已被广泛用于为自主移动系统创造运动路径。在这项工作中,我们延长有控制障碍功能的基于时间的RRTs(CBFs),以便在充满活力的环境中与许多行人一起制定安全运动计划。我们的框架基于一个非常适合室内狭窄环境的人类运动预测模型。我们展示了我们对于丰田人类支持机器人在狭窄走廊飞行的高不忠模式的态度。我们在三种情景中显示,我们提议的在线方法可以在动态不明的移动物剂面前安全地导航。