human-aware cbf enforcement

Designing high-order control barrier functions (CBFs) for complex nonlinear dynamical systems remains challenging, and conventional hyperplane-based approximations of unsafe regions often yield overly conservative control policies. To address this, we propose a unified optimization framework that jointly tunes CBF parameters and control inputs. Our key innovation is the “minimally restrictive hyperplane CBF,” which employs continuous parametrization and co-optimization to guarantee strict safety while maximizing control freedom. The method accommodates both static and dynamic obstacles and explicitly incorporates practical actuation constraints—such as acceleration limits. Evaluated on a double-integrator system, our approach significantly improves trajectory flexibility and obstacle avoidance robustness compared to baseline methods, achieving a superior trade-off between safety and performance.

This work addresses the lack of formal collision-avoidance guarantees for learning-based motion planners in complex road environments. We propose a real-time safety filter grounded in Control Barrier Functions (CBFs), enabling rigorous safety enforcement without compromising planning fidelity. Our method is the first to embed exact, non-conservative safety constraints for arbitrarily shaped road boundaries—eliminating the need for geometric approximations. Safety is enforced via online minimal-intervention quadratic programming (QP), which rectifies control commands while preserving the planner’s original intent. Extensive evaluation across challenging scenarios—including high-curvature, multi-branch, and narrow road segments—demonstrates 100% safety compliance and an average computation frequency of 40 Hz. The implementation, including source code and demonstration videos, is publicly available.

This work addresses the gap between theoretical safety guarantees and practical feasibility of Control Barrier Functions (CBFs) in real-world systems subject to input constraints, where implicit assumptions often render CBFs ineffective. By systematically distinguishing between candidate and valid CBFs, the study uncovers the true source of safety in passive systems and extends safety verification to non-passive systems. Integrating system dynamics, explicit input constraint modeling, and class-K function analysis, the authors establish precise conditions under which CBFs yield valid safety assurances in low-dimensional systems and derive actionable design principles for safe controllers. An accompanying interactive web platform visually illustrates the core mechanisms and common pitfalls, offering practitioners an intuitive guide for reliable deployment.

This work addresses the myopic nature of traditional Control Barrier Functions (CBFs) and their difficulty in simultaneously satisfying safety requirements and control constraints. The authors propose Predictive Flow Control Barrier Functions (P-CBF), which extend safety verification over the entire predicted trajectory by integrating a terminal backup safe set with a planning time-shift mechanism to jointly optimize both the control policy parameters and real-time inputs. By employing an adjustable prediction horizon, the method enables end-to-end trajectory safety certification while unifying finite-horizon cost optimization with safety guarantees. Under convex polyhedral control constraints, the resulting problem reduces to a quadratic program (QP), amenable to efficient real-time solution. Experimental results on nonholonomic ground robots in dense navigation scenarios demonstrate that the proposed FlowBarrier approach achieves the highest goal-reaching success rate, zero safety violations, and the lowest computation time across 100 trials.

This work addresses the inherent conflict between safety and performance objectives in physical human–robot interaction by proposing a novel framework that integrates Control Barrier Functions (CBFs) with Hierarchical Quadratic Programming (HQP). The approach flexibly embeds safety and performance tasks across multiple HQP layers and incorporates a hierarchical relaxation mechanism to effectively resolve task conflicts, thereby significantly enhancing system feasibility and adaptability. Experimental validation on a real redundant robotic platform demonstrates that the proposed method simultaneously enforces real-time safety constraints and achieves high-performance interactive behaviors, highlighting its strong generality and practical utility.

Construction automation increasingly requires autonomous mobile robots, yet robust autonomy remains challenging on construction sites. These environments are dynamic and often visually occluded, which complicates perception and navigation. In this context, valuable information from audio sources remains underutilized in most autonomy stacks. This work presents a control barrier function (CBF)-based safety filter that provides safety guarantees for obstacle avoidance while adapting safety margins during navigation using an audio-derived risk cue. The proposed framework augments the CBF with a lightweight, real-time jackhammer detector based on signal envelope and periodicity. Its output serves as an exogenous risk that is directly enforced in the controller by modulating the barrier function. The approach is evaluated in simulation with two CBF formulations (circular and goal-aligned elliptical) with a unicycle robot navigating a cluttered construction environment. Results show that the CBF safety filter eliminates safety violations across all trials while reaching the target in 40.2% (circular) vs. 76.5% (elliptical), as the elliptical formulation better avoids deadlock. This integration of audio perception into a CBF-based controller demonstrates a pathway toward richer multimodal safety reasoning in autonomous robots for safety-critical and dynamic environments.

This work addresses the limitations of traditional Safety Speed Models (SSMs), which assume constant human velocity and often yield inaccurate predictions of minimum human–robot separation, leading to unnecessary stops. To overcome this, the authors propose a novel safety controller that, for the first time, incorporates human acceleration into Control Barrier Functions (CBFs). By analytically computing the minimum separation distance under worst-case braking scenarios, the method embeds this distance as an inequality constraint within a Sequential Quadratic Programming (SQP) framework, enabling task-scaling safety guarantees. Validated on a UR10e platform with a PD safety filter and spatial tube constraints, the approach reduces average trajectory error by 63% compared to baseline methods while significantly mitigating over-conservative avoidance behaviors—all within compliance with ISO 10218 standards—thereby enhancing collaborative efficiency.

This work addresses the challenges of multi-constraint handling and computational complexity in whole-body collision avoidance for robots operating in high-dimensional configuration spaces. The authors propose a real-time obstacle avoidance framework based on a 3D Poisson safety function. By leveraging occupancy data of the environment, surface points on the robot arm are sampled, and a buffered free space is constructed via the Pontryagin difference. Within this region, a Poisson equation is solved to generate a globally smooth safety function. Integrating control barrier functions with a multi-constraint quadratic program, the approach reduces the intricate whole-body collision avoidance problem to a single smooth constraint. Theoretical analysis demonstrates that ensuring safety at the sampled surface points guarantees collision-free motion for the entire continuous manipulator. Experiments on a 7-DOF robotic arm validate the method’s efficacy and reliability in achieving real-time whole-body obstacle avoidance in dynamic environments.

This work addresses the challenge of safely transferring safety guarantees between heterogeneous systems with mismatched dynamics by proposing a transfer Control Barrier Function (tCBF) framework. The approach systematically migrates safety constraints from a source system to a target system by integrating a simulation function with an explicit margin term, which compensates for model mismatch. Safety is enforced via a quadratic programming-based safety filter that minimally modifies the nominal control input. Notably, this method achieves cross-system safety certificate transfer without requiring assumptions on matching state dimensions or dynamical structures. The explicit margin ensures robustness against model discrepancies, thereby preserving safety in the target system. The efficacy of tCBF is demonstrated in a quadrotor obstacle avoidance task, where safety constraints are successfully transferred with negligible interference to the original controller, highlighting the framework’s generality and practical utility.