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

2026-02-27

AI Summary

This work addresses autonomous obstacle avoidance in dynamic environments where perception is limited to local sensing. The authors propose an end-to-end hierarchical safety filtering framework that introduces the Poisson Safety Function (PSF) as a Control Barrier Function (CBF). Safety constraints are sequentially enforced at two stages—predicted trajectory and real-time velocity—to provide formal safety guarantees for full-order robotic systems. By integrating local point cloud mapping, multi-stage safety filtering, and full-order control, the approach significantly enhances both obstacle avoidance performance and robustness. Extensive experiments across multiple legged robot platforms and real-world dynamic scenarios demonstrate the method’s generality and superiority. Pareto analysis further confirms that it outperforms conventional single-stage safety filters by achieving a better trade-off between safety and motion performance.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Safety and Robustness
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

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.

Problem

• autonomous collision avoidance
• layered safety
• control barrier function
• local perception
• dynamic scenarios

Innovation

• Layered Safety
• Control Barrier Function (CBF)
• Poisson Safety Function
• Multistage Safety Filter
• Dynamic Collision Avoidance