让机器人在视线被遮挡时仍能安全避障，提前预判隐藏障碍物的可能位置。
OcclusionCBF: Backup Control Barrier Functions for Safe Navigation Among Hidden Dynamic Obstacles
- 基于可达占用预测构建安全滤波器，提前防御隐藏动态障碍物
- 实测在随机测试与真实硬件上成功率显著高于现有方法，计算仅需毫秒级
- 适用于自动驾驶、服务机器人等需在遮挡环境中安全导航的场景
在视线受阻环境下，机器人可能进入无法避免动态障碍物碰撞的状态。本文提出OcclusionCBF，一种将备份控制屏障函数扩展至潜在隐藏动态障碍物的可达占用预测的安全滤波机制。该方法通过认证预设的备份轨迹对抗膨胀后的占用区域，并确保终端集的可验证性，生成线性约束用于最小侵入式的二次规划滤波。我们证明了该安全滤波器的递归可行性，以及对所有覆盖于占用预测中的隐藏障碍物运动模式均能实现碰撞规避。随机基准测试、MetaUrban仿真及硬件实验表明，该方法在毫秒级计算开销下，任务成功率达显著优于反应式与遮挡感知预测基线。
原文摘要 · Abstract (English)
Robots navigating under occlusion may enter states from which no admissible input can avoid a dynamic obstacle once it becomes visible. We present OcclusionCBF, a safety filter that extends backup control barrier functions to reachable-occupancy predictions for potentially hidden dynamic obstacles in occluded regions. The method certifies a prescribed backup rollout against collision-inflated occupancy and a verified terminal set, yielding affine constraints for minimally invasive quadratic-program filtering. We establish recursive feasibility of the resulting safety filter, and collision avoidance for every hidden-obstacle motion covered by the occupancy prediction. Randomized benchmarks, MetaUrban simulations, and hardware experiments demonstrate improved task success over reactive and occlusion-aware predictive baselines with millisecond-scale computation.
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