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

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.

The first two authors contributed equally to this work. Project page: this https URL

Robotics (cs.RO); Systems and Control (eess.SY)

arXiv:2609.06342 [cs.RO]

(or arXiv:2609.06342v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.06342 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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