用激光雷达实时保障机器人在狭窄动态环境中的安全导航
Reactive Robot-Centric Safety for Autonomous Navigation in Constrained and Dynamic Environments
- 将激光雷达感知与控制屏障函数结合，动态生成避障约束
- 可在控制频率下处理大量约束，且不影响正常任务执行
- 适合四足机器人在地下等复杂场景中执行巡检任务
本文针对空间受限的动态环境中自主机器人导航的实时安全性问题，仅依赖机载传感器提出一种实时控制架构。该架构将基于3D LIDAR感知的复合控制屏障函数（CBF）安全滤波器直接集成到自主系统流程中。所提出的感知驱动框架通过机载点云数据动态施加碰撞规避约束，能够在控制频率下处理大量约束，同时对正常任务执行干扰极小。安全区域定义为机体坐标系下的椭球体，与平台几何一致，随机器人旋转在世界坐标系中产生时变约束；该效应通过为每个激光雷达点设计专用的时变CBF公式进行处理。通过在地下环境中使用四足机器人执行视觉巡检任务的多组实地实验验证了系统性能，证明其在存在动态障碍物、不安全高层指令、突发定位异常及穿越狭窄通道时仍能可靠运行。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。