Reactive Robot-Centric Safety for Autonomous Navigation in Constrained and Dynamic Environments

TL;DR

This paper introduces a real-time, perception-driven safety control framework for autonomous robots navigating constrained, dynamic environments using onboard sensors and a composite control barrier function approach.

Contribution

It presents a novel perception-based safety filter integrated into the autonomy pipeline, handling numerous constraints dynamically from onboard LIDAR data.

Findings

Validated through underground field experiments with a quadruped robot

Demonstrated reliable obstacle avoidance and safety in complex environments

Handled dynamic obstacles and localization anomalies effectively

Abstract

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.