OcclusionCBF: Backup Control Barrier Functions for Safe Navigation Among Hidden Dynamic Obstacles
Authors: Taekyung Kim, Hun Kuk Park, Renya Wada, Nikolay Atanasov, Shumon Koga, Dimitra Panagou
Organizations: Department of Robotics, University of Michigan, Ann Arbor, MI 48109, USA · Department of Computer Science and Systems Engineering, Kobe University, Hyogo, Japan · Department of Electrical and Computer Engineering, University of California, San Diego, CA 92093, USA · Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109, USA
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.
Figures & tables
Fig. 1: Illustrative comparison of safety filtering with and without occlusion awareness for a double-integrator robot at a blind crossing. (a) A conventional CBF-QP considers only detected vehicles and follows the nominal command while the approaching vehicle remains occluded, reaching a state with insufficient control authority to avoid collision after detection. (b) OcclusionCBF propagates the admissible motion of potentially hidden vehicles, certifies a backup rollout against the resulting reachable occupancy, and minimally modifies the nominal input to slow down and increase clearance before entering the crossing.
Fig. 2: Geometric interpretation for one occluded region. (a) The occluded region is enclosed by O(j)(t) and inflated by rcol to H(j)(t,0) ; hjC(y,t,0) is constructed from its facet margins. (b) The occupancy expands with the look-ahead time s while the backup rollout remains outside it and reaches S0(t+T) with terminal clearance ρT .
Fig. 3: Representative successful OCBF-QP trial for the double integrator with Nobs=50 . The global view shows the completed waypoint route, while the local views show representative occlusion interactions and backup rollouts.
Fig. 4: Benchmark results over 100 paired trials for the double integrator (top) and unicycle (bottom) as Nobs increases. OCBF-QP attains the highest hard-constrained success rate at every tested density.
Fig. 5: High-fidelity simulation and hardware evaluations. (a) MetaUrban demonstrations for the unicycle (left) and double-integrator quadruped (right), with robot-view snapshots above and trajectories below. (b) Hardware blind crossing: the occlusion-agnostic CBF-QP collides after late detection, whereas OcclusionCBF intervenes before detection and reaches the goal.
Method
Unicycle ( Nobs )
Double integrator ( Nobs )
10
20
30
10
30
50
Control-Tree MPC [ 9 ]
48.3
54.2
58.9
106.7
110.7
117.0
Single-Hypothesis MPC
7.3
8.1
8.7
13.0
13.8
15.4
OA-MPC [ 2 ]
69.2
101.5
117.5
93.8
151.4
186.9
OACP [ 3 ]
80.5
76.9
78.5
87.4
89.4
88.8
CBF-QP [ 11 ]
0.7
0.7
0.8
0.6
0.7
0.8
TABLE I: Average controller computation time per update ( ms ).