Reactive Robot-Centric Safety for Autonomous Navigation in Constrained and Dynamic Environments
For autonomous robots operating in real-world constrained environments, this work provides a minimally invasive safety filter that handles many constraints at control frequency using only onboard sensors.
This work presents a real-time control architecture integrating a 3D LIDAR perception-based composite control barrier function (CBF) safety filter for autonomous navigation in constrained dynamic environments. Field experiments on a quadruped platform demonstrated reliable collision avoidance in narrow corridors, with dynamic obstacles, and during localization anomalies.
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.