Multi-robot safety filters get exact conflict diagnosis and fix guidance
Exact Feasibility Certification and Optimal Responsibility Allocation for Multi-Robot CBF Safety Filters
RoboticsMultiagent Systems
Summary
Robots working together need to be safe, but their safety controllers sometimes stop working because of conflicts. The authors develop a tool that tells exactly why the safety checks fail and which robots cause the problem. They also create a way to share safety responsibilities fairly so robots are less likely to get stuck. Their tests show fewer failures and safer robot runs, and the tool can suggest which safety rule to relax to fix issues.
What this means in practice
- •For industrial automation teams: Prevent safety filter failures and identify problem robots during cooperative tasks with multiple robots on factory floors.
- •For autonomous vehicle operators: Improve multi-vehicle safety systems by pinpointing conflicts and optimizing shared safety constraints in real-time.
Authors
Chandan Kumar Sah, Jishnu Keshavan
Abstract
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 the agents and interactions responsible for the conflict. We further propose an algorithm to optimally allocate shared safety constraints by maximizing the worst local feasibility margin, yielding a linear program for polyhedral input sets. In $320$ paired closed-loop simulations, the proposed allocation reduces infeasible control steps from roughly $50\%$ to $6.2\%$, and reduces safety-violating runs from $118/160$ to $24/160$. In addition, across $52$ infeasibility events, the certificate identifies an interaction whose relaxation restores feasibility in $94\%$ of cases.