Layered Safety: Enhancing Autonomous Collision Avoidance via Multistage CBF Safety Filters

TL;DR

This paper introduces a layered safety filtering framework for autonomous robots that combines predictive and real-time control barrier functions to enhance collision avoidance using local perception data.

Contribution

The paper presents a novel multistage safety filter architecture that improves robustness and formal safety guarantees in dynamic collision avoidance tasks.

Findings

Outperforms traditional single-stage safety filters in Pareto analysis.

Demonstrates effectiveness on multiple legged robot platforms.

Provides formal safety guarantees assuming accurate velocity tracking.

Abstract

This paper presents a general end-to-end framework for constructing robust and reliable layered safety filters that can be leveraged to perform dynamic collision avoidance over a broad range of applications using only local perception data. Given a robot-centric point cloud, we begin by constructing an occupancy map which is used to synthesize a Poisson safety function (PSF). The resultant PSF is employed as a control barrier function (CBF) within two distinct safety filtering stages. In the first stage, we propose a predictive safety filter to compute optimal safe trajectories based on nominal potentially-unsafe commands. The resultant short-term plans are constrained to satisfy the CBF condition along a finite prediction horizon. In the second stage, instantaneous velocity commands are further refined by a real-time CBF-based safety filter and tracked by the full-order low-level robot controller. Assuming accurate tracking of velocity commands, we obtain formal guarantees of safety for the full-order system. We validate the optimality and robustness of our multistage architecture, in comparison to traditional single-stage safety filters, via a detailed Pareto analysis. We further demonstrate the effectiveness and generality of our collision avoidance methodology on multiple legged robot platforms across a variety of real-world dynamic scenarios.