Organizations: School of Automation, Northwestern Polytechnical University, Xi’an, China · Department of Robotics, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates
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.
Figures & tables
Fig. 1: Motivation and core idea of BIG-CBF. A conventional CBF can preserve local safety yet fail to select a productive avoidance direction, resulting in a safe-but-stalled state in geometrically ambiguous scenes. In layered navigation, a branch that appears feasible under nominal geometry may be repeatedly corrected at execution time when the downstream CBF accounts for uncertainty. BIG-CBF addresses both issues by selecting among a library of closed-loop behaviors through low-rate behavior imagination, while exposing shared uncertainty components to both the imagination and high-rate hard-CBF execution layers.
Fig. 2: Overview of BIG-CBF. The low-rate imagination layer evaluates six closed-loop feedback behaviors and selects only a behavior mode b∗ . At the high-rate execution loop, the selected behavior recomputes a state-dependent local subgoal and uin from current measurements, after which the hard CBF generates usafe . Shared uncertainty components are used for both behavior-level compatibility evaluation and execution-level filtering.
TABLE II: Aggregated Performance Across 9 Scenarios
Fig. 4: Scenario-level performance across the nine simulation environments. The largest success-rate differences occur in topologically ambiguous scenarios such as required sidestepping and multi-static clutter, while several simpler scenarios saturate at 100%. All methods use the same final hard CBF execution filter.
Fig. 5: Representative real-world executions of BIG-CBF in three navigation scenarios: (a) required sidestepping, (b) static clutter, and (c) pedestrian crossing. For each scenario, four snapshots are shown from left to right in temporal order. The sequences illustrate how BIG-CBF maintains a persistent local maneuver while continuously updating the executed command from current onboard perception. Full temporal executions are provided in the supplementary video.
Method
Success
Time
Clearance
ECBF
Active
QP Infeas.
(s)
(m)
(%)
(%)
CBF-only
7/15
21.62
0.29
11.04
88.60
0.12
Backup-CBF + CBF
8/15
19.80
0.21
1.65
59.60
0.09
PL-CBF + CBF
9/15
19.92
0.16
1.49
55.20
0.43
APF + CBF
1/15
29.75
0.29
0.28
29.60
0.04
DWA + CBF
13/15
12.32
0.14
0.88
47.30
1.42
TABLE III: Overall performance in 3 real-world scenarios
GRASP Laboratory, University of Pennsylvania, Philadelphia, PA, USA · Department of Computer Science, University of Illinois Chicago, Chicago, IL, USA · Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC, USA