基于可验证约束缩减的可扩展多智能体安全控制：管收紧策略
Scalable Tube-Tightened Multi-Agent Safety via Certified Constraint Reduction
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中文总结 AI 辅助
该研究针对多智能体系统的分布式模型预测控制，提出可验证的约束缩减方法，减少安全约束数量，保留标称控制，提升计算效率且保证安全。
中文摘要 AI 辅助
本文针对多智能体系统中基于管收紧指数控制障碍函数（eCBF）的分布式模型预测控制，提出了一种可验证的约束缩减方法。在每个预测阶段，智能体-智能体、智能体-障碍物的成对eCBF条件定义了局部控制空间中的半空间。该方法并非强制所有半空间，而是保留几何自适应子集，并通过Farkas引理验证缩减后的容许集包含于完整收紧集内。对于平面输入，通过最大角间隙表征锥覆盖：严格半平面情形下仅需两个极端方向，其他几何情形初始保留三个约束，仅当验证失败时才增加约束。锥乘子与标称感知偏移以闭式形式获取，无需辅助优化，且该构造保留了所有对完整收紧集已容许的标称控制。因此，缩减后的控制器继承了底层管-eCBF公式的鲁棒安全保证。在含10个跟随者、4个障碍物的研究中，该方法平均保留更少的安全约束，重现了完整滤波器的标称接受/拒绝决策且无真实安全违规，且随着约束数量与预测时域增加，计算增益不断提升。
英文摘要
This paper develops a certified constraint-reduction method for distributed model predictive control with tube-tightened exponential control barrier functions (eCBFs) in multi-agent systems. At each prediction stage, pairwise agent--agent and agent--obstacle eCBF conditions define halfspaces in the local control space. Rather than enforcing all such halfspaces, a geometry-adaptive subset is retained and a Farkas certificate verifies that the reduced admissible set is contained in the full tightened set. For planar inputs, cone coverage is characterized through the largest angular gap: two extreme directions suffice in the strict half-plane regime, while other geometries initialize with three retained constraints and escalate only when certification fails. Conic multipliers and nominal-aware offsets are obtained in closed form, without an auxiliary optimization, and the resulting construction preserves any nominal control already admissible for the full tightened set. Consequently, the reduced controller inherits the robust safety guarantee of the underlying tube-eCBF formulation. In a ten-follower, four-obstacle study, the method retained fewer safety constraints on average, reproduced the full filter's nominal accept/reject decisions with no true safety violations, and achieved increasing computational gains as the constraint count and prediction horizon grew.
