反应式机器人中心安全机制用于受约束和动态环境中的自主导航
Reactive Robot-Centric Safety for Autonomous Navigation in Constrained and Dynamic Environments
AI总结：
本文提出一种基于3D激光雷达感知的复合控制屏障函数安全过滤器，用于实时保障自主机器人在受限动态环境中的安全导航，通过现场实验验证其在复杂环境下的可靠性。
摘要：
本文研究了如何利用仅有的机载传感器确保自主机器人导航在空间受限动态环境中的实时安全性。我们提出了一种实时控制架构，将基于3D激光雷达感知的复合控制屏障函数（CBF）安全过滤器直接集成到自主系统中。该感知驱动框架通过机载点云数据动态施加避障约束，从而在控制频率下处理大量约束，同时对常规任务执行保持最小侵入性。安全区域定义为与平台几何一致的体坐标系下的椭球体，这在机器人旋转时会在世界坐标系中诱导时间变化约束；通过为每个激光雷达点专门制定时间变化（CBF）处理方式来应对这一影响。通过在地下环境中进行多场实验，利用四足平台执行视觉检查任务，验证了系统在动态障碍、不安全高层参考、突然定位异常以及狭窄走廊穿越情况下的可靠运行。
英文摘要：
In this work, we address the problem of ensuring real-time safety in autonomous robot navigation, in spatially constrained dynamic environments, by utilizing only onboard sensors. We present a real-time control architecture that integrates a 3D LIDAR perception-based composite control barrier function(CBF)-based safety filter directly into the autonomy pipeline. The proposed perception-driven framework enforces collision avoidance constraints dynamically from onboard point cloud data, thus allowing a large number of constraints to be handled at the control frequency, while remaining minimally invasive to nominal task execution. The safety region is defined as an ellipsoid in the body-frame, consistent with the geometry of the platform, which induces time-varying constraints in the world frame as the robot rotates; this effect is handled through a dedicated formulation of time-varying (CBF) for each LIDAR point. We validate the system through multiple field experiments in underground environments by utilizing a quadruped platform performing a visual inspection task, demonstrating reliable operation in the presence of dynamic obstacles, unsafe high-level references, abrupt localization anomalies, and while traversing through narrow corridors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
