四旋翼无人机高阶安全关键控制的可行性与奇异性
Feasibility and Singularity in High-Order Safety-Critical Control for Quadrotor UAVs
- Universidad Politécnica de Madrid（马德里理工大学）
- Centre for Automation and Robotics (CSIC-UPM)（自动化与机器人中心（西班牙国家研究委员会-马德里理工大学））
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对四旋翼团队在有界输入和避碰约束下的高阶安全控制，提出扭矩感知动态扩展与高斯过程学习残差的方法，解决推力失效和联合不可行问题，保证碰撞避免。
AI中文摘要：
我们研究了在有界输入和成对碰撞避免约束下四旋翼无人机团队的高阶安全关键控制。当相对位移与可用推力方向正交时，平方距离障碍可能失去推力有效性，而奇异约束在共享界限下可能仍然联合不可行。我们通过成对有效性和聚合可行性度量来刻画这两种现象。一种扭矩感知的动态扩展在四阶障碍中暴露姿态扭矩，并在正推力下防止扩展输入行消失。高斯过程直接学习四阶HOCBF残差，提供鲁棒裕度而无需对未知扰动进行微分。在残差有界和持续可行性假设下，所得到的QP保证碰撞避免，并在满足鲁棒安全和执行器约束时恢复名义输入。
英文摘要：
We study high-order safety-critical control of quadrotor teams under bounded inputs and pairwise collision-avoidance constraints. Squared-distance barriers may lose thrust effectiveness when the relative displacement is orthogonal to the available thrust directions, while nonsingular constraints may still be jointly infeasible under shared bounds. We characterize both phenomena through pairwise effectiveness and aggregate feasibility measures. A torque-aware dynamic extension exposes attitude torques in a fourth-order barrier and prevents the extended-input row from vanishing under positive thrust. Gaussian processes directly learn the fourth-order HOCBF residual, providing robust margins without differentiating unknown perturbations. Under residual-bound and persistent-feasibility assumptions, the resulting QP guarantees collision avoidance and recovers the nominal input whenever it satisfies the robust safety and actuator constraints.
