High-order safety control improves collision avoidance for quadrotor teams
Feasibility and Singularity in High-Order Safety-Critical Control for Quadrotor UAVs
Robotics
Summary
Controlling multiple quadrotor drones to avoid collisions is tricky because their movement and thrust directions limit how they can respond. The authors show problems with existing safety rules that sometimes fail in certain flight situations. They develop a new approach that uses a fourth-order safety rule aware of drone torque and learn unknown disturbances with machine learning to keep drones safe. Their method ensures drones avoid collisions while following safety and input limits and can return to normal flight when conditions allow.
What this means in practice
- •For drone fleet operators: Maintain safe distances and prevent collisions in multi-drone operations under physical thrust and torque limitations.
- •For autonomous vehicle engineers: Incorporate high-order safety controls that adapt to unknown environmental disturbances for robust vehicle navigation.
Authors
Omayra Yago Nieto, Leonardo Colombo
Abstract
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.