OcclusionCBF: Backup Control Barrier Functions for Safe Navigation Among Hidden Dynamic Obstacles

2026-09-06

AI Summary

针对机器人在遮挡环境下导航时可能遇到的动态障碍物问题，提出OcclusionCBF方法，通过扩展备份控制屏障函数来预测潜在障碍并确保安全导航。

Abstract

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.

Problem

• Occlusion
• Dynamic Obstacles
• Collision Avoidance

Innovation

• Backup Control Barrier Functions
• Reachable-Occupancy Predictions
• Collision Avoidance