外观
论文arXiv 2603.003381 个月前
分层安全：通过多阶段 CBF 安全滤波器增强自主避碰
原文链接：Layered Safety: Enhancing Autonomous Collision Avoidance via Multistage CBF Safety Filters（arXiv 2603.00338 · 发布日期：2026-09-22）
Layered Safety: Enhancing Autonomous Collision Avoidance via Multistage CBF Safety Filters
提出基于局部感知点云、PSF与多阶段CBF安全滤波的动态避碰框架，并在多个足式机器人平台的真实动态场景中验证。
核心要点
- 面向仅依赖局部感知数据的广泛动态避碰应用，构建鲁棒可靠的分层安全滤波。
- 由机器人中心点云生成占据图并合成PSF，将其作为两级CBF安全滤波的屏障函数。
- 预测滤波先优化安全短期轨迹，实时CBF滤波再细化瞬时速度并由底层控制跟踪。
- 通过Pareto分析对比单阶段滤波验证最优性与鲁棒性，并展示避碰方法的有效性和通用性。
验证范围与局限：安全保证建立在速度指令可被准确跟踪的假设上；验证范围为多个足式机器人平台上的多种真实动态场景。
英文摘要 (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.
作者：Erina Yamaguchi, Ryan M. Bena, Gilbert Bahati, Aaron D. Ames
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