SOURCE-LINKED INTELLIGENCE
Exact Feasibility Certification and Optimal Responsibility Allocation for Multi-Robot CBF Safety Filters
Multi-robot Control Barrier Function (CBF) safety filters can become infeasible, but a failed quadratic program (QP) does not indicate why the conflict occurred or how to resolve it. To address this, we develop an exact feasibility certificate for multi-agent CBF filters with heterogeneous control-affine dynamics and convex input sets. The certificate quantifies a feasibility reserve by separating the demand imposed by safety constraints from the available actuator supply. This decomposition shows when CBF gain tuning or increased actuation can and cannot resolve infeasibility, and identifies
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-14T02:29:38.000Z
First collected: 2026-09-20T12:21:05.240Z. This is not the publication date.