Control method improves safety under limits in acceleration tasks
Joint-Feasibility Margin Feedback for Bounded-Input Second-Order Control Barrier Functions
Robotics
Summary
Robots and other systems often need to follow safety rules while moving, even when their ability to accelerate is limited. The authors developed a way to check if multiple safety rules can still be met together with the available acceleration and to predict which control commands will keep the system safe in the near future. Their method picks controls that maintain a positive safety margin, helping avoid conflicts between rules that could make safe motion impossible. Tests show this approach keeps system commands feasible and tasks achievable even in challenging conditions.
What this means in practice
- •For robot motion planners: Maintain feasible and safe trajectories when robots must obey multiple acceleration limits and safety constraints simultaneously.
- •For automation engineers: Design controllers that predict and avoid conflicts among multiple safety rules under input constraints in industrial machines.
Authors
Siyuan Yang, Shaoming He
Abstract
Multiple second-order control barrier function constraints can exhaust their common control authority under bounded acceleration, even when the current state remains feasible. We define a signed local joint-feasibility margin whose sign exactly identifies whether the current barrier half-spaces intersect the admissible input set. For unit-scaled distance constraints, it has acceleration units and a support-function dual that identifies weighted constraint conflicts. We predict the margin one sample ahead for nine task-directed candidates, selecting either the least-deviating candidate that attains a desired margin or the candidate with the largest predicted margin before solving the same hard HOCBF quadratic program. A controlled one-step case shows that, at the state predicted under the hard-QP command, two constraints remain individually feasible but become jointly infeasible, whereas the margin-feedback controller retains a positive next margin. Separately, across 20 paired initial conditions, the margin-feedback controller retains the hard-QP command in a low-pressure task and acts at positive margin in a critical task before the hard-QP problem becomes infeasible; its trajectories remain feasible at that time and complete the task. Source code is publicly available. https://github.com/xirhxq/cbf/tree/robio2026-preventive-regulation