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
Safe navigation of autonomous robots remains one of the core challenges in the field, especially in dynamic and uncertain environments. One prevalent approach is safety filtering based on control barrier functions (CBFs), which are easy to deploy but difficult to design. Motivated by the shortcomings of existing learning- and model-based methods, we propose a simple yet effective neural CBF design method for safe robot navigation in dynamic environments. We employ the idea of a composite CBF, where multiple neural CBFs are combined into a single CBF. Individual CBFs are trained using data generated offline via the Hamilton-Jacobi reachability framework to approximate the optimal safe set for single moving obstacles. Additionally, we use a residual neural architecture, ensuring that the estimated safe set does not intersect with the corresponding failure set. The method is extensively evaluated in simulation experiments for a ground robot and a quadrotor, comparing it against several baseline methods. The proposed method improves success rates by up to 18% over the strongest baseline, while maintaining comparable or lower path lengths and motion times. The method is also demonstrated in hardware experiments for both types of robots.
Bojan Derajić, Sebastian Bernhard, Wolfgang Hönig
Technical University of Berlin, Germany · AUMOVIO, Germany · Robotics Institute Germany (RIG), Germany
Safe navigation often relies on well-defined conditions based on the shape of robots and obstacles, and can be challenging when they have irregular geometries. While Control Barrier Functions (CBFs) offer an efficient mechanism to enforce safe set forward invariance, common shape surrogates (e.g., spheres or super-ellipsoids) either are overly conservative in unstructured scenes or require many local primitives, which inflates constraint counts and degrades real-time performance. In this paper, we introduce a novel geometry-aware Control Barrier Function (CBF) based on Bernstein-Polynomial Signed Distance Fields (BP-SDFs). It provides a unified way to represent the obstacles and robots, so as to represent the barrier function with a unified minimum distance. Benefiting from the differentiability of the Bernstein polynomials, one can easily enforce the control constraints in a closed loop. We validate the method's efficiency and performance to guarantee safety in single-robot navigation and heterogeneous multi-robot collision avoidance via simulations under different environments.
Siwon Jo, Yanze Zhang, Yupeng Yang +1
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
Safe control in unknown environments is a key challenge in mobile robotics. Control Barrier Functions (CBFs) provide a principled framework for guaranteeing safety constraint satisfaction. State-of-the-art CBF approaches assume either known environments with predefined obstacles, or rely only on obstacles currently within the robot's Field of View (FoV). However, practical robots in a priori unknown environments can observe their surroundings only partially, and therefore can violate safety due to limited FoV, sensor range, or occlusion. This paper incorporates the memory of previously observed obstacles of arbitrary shape that have left the robot's FoV into CBF-based safe control. In particular, we couple the Signed Distance Function (SDF)-based CBF formulation to an occupancy grid map built online during the system's operation. Furthermore, the lack of steering authority induced by the SDF gradient degeneracy when facing obstacles head-on is addressed by constructing a Gaussian pyramid of the SDF, yielding a multi-level CBF. The efficacy of the proposed approach is evaluated against memory unaware baselines in the CARLA simulator. Moreover, we demonstrate the generalizability of the proposed approach in real deployments on a small warehouse robot and a large, articulated frame steering autonomous wheel loader.
Golnaz Raja, Miloš Prágr, Topi Reino Johannes Kärki +2
Faculty of Engineering and Natural Sciences, Tampere University, Kalevantie 4, 33100 Tampere, Finland