BIG-CBF：行为想象引导的控制障碍函数与共享不确定性用于移动机器人导航

中文摘要 AI 辅助

控制障碍函数（CBF）为自主移动机器人实施局部避碰约束提供了数学基础框架，通常通过基于优化的安全滤波器实现。然而，最小干预的CBF滤波器缺乏任务级机动意识，当多个不同机动在局部均可行时，可能无法选择有效的避让方向，导致在几何模糊环境中出现安全但停滞的行为。本文提出BIG-CBF，即行为想象引导的控制障碍函数与共享不确定性，这是一种双速率导航架构，将低速率机动选择与高速率安全滤波分离。在短时域内，想象并评估六种闭环反馈行为，使用解析CBF兼容性以及考虑任务进展、冻结、平滑性和切换的轻量级目标函数。为减少规划与执行的不匹配，想象层和执行层共享相对运动延迟、障碍预测、零阶保持运动和命令执行残差的一致不确定性来源，而硬CBF保持最终安全权威。在涵盖九个场景的3,600回合比较基准中，BIG-CBF实现了最高的总体任务成功率99.78%，同时大幅减少下游CBF干预。在搭载机载Jetson Orin Nano计算的物理全向机器人上，BIG-CBF完成所有15次评估运行，无记录接触事件。与非共享变体的匹配硬件比较进一步显示更低的CBF干预能量和激活频率，支持机动选择与安全关键执行之间一致性的改善。

英文摘要

Control barrier functions (CBFs) provide a mathematically grounded framework for enforcing local collision-avoidance constraints in autonomous mobile robots, commonly through optimization-based safety filters. However, a minimum-intervention CBF filter lacks task-level maneuver awareness and may fail to select a productive avoidance direction when multiple distinct maneuvers are locally viable, leading to safe but stalled behavior in geometrically ambiguous environments. This paper presents BIG-CBF, Behavior-Imagination-Guided Control Barrier Function with shared uncertainty, a two-rate navigation architecture that separates low-rate maneuver selection from high-rate safety filtering. Over a short horizon, six closed-loop feedback behaviors are imagined and evaluated using analytic CBF compatibility together with a lightweight objective accounting for task progress, freezing, smoothness, and switching. To reduce planning-execution mismatch, the imagination and execution layers share consistent uncertainty sources for relative-motion delay, obstacle prediction, zero-order-hold motion, and command-execution residuals, while a hard CBF remains the final safety authority. In a 3,600-episode comparative benchmark across nine scenarios, BIG-CBF achieves the highest overall task success rate of 99.78% while substantially reducing downstream CBF intervention. On a physical omnidirectional robot with onboard Jetson Orin Nano computation, BIG-CBF completes all 15 evaluation runs without a recorded contact event. Matched hardware comparisons against the non-shared variant further show lower CBF intervention energy and activation frequency, supporting improved consistency between maneuver selection and safety-critical execution.

发表机构

• Northwestern Polytechnical University（西北工业大学）
• Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）

机构由 AI 辅助整理，请以论文原文为准。