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

• URL: http://arxiv.org/abs/2603.00338v1
• Date: Fri, 27 Feb 2026 22:19:45 GMT
• Title: Layered Safety: Enhancing Autonomous Collision Avoidance via Multistage CBF Safety Filters
• Abstract summary: This paper presents a general end-to-end framework for constructing robust and reliable layered safety filters.<n>Given a robot-centric point cloud, we begin by constructing an occupancy map which is used to synthesize a Poisson safety function (PSF)<n>The PSF is employed as a control barrier function (CBF) within two distinct safety filtering stages.
• Score: 18.386652442201665
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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