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This research advances safety-critical control theory for quadrotor teams by addressing limitations in high-order barrier functions and introducing torque-aware dynamic extensions to prevent input vanishing. By leveraging Gaussian processes to learn residuals, the proposed method ensures robust collision avoidance without relying on differentiating unknown perturbations. The work represents a significant theoretical contribution to robotics control optimization rather than a general AI application or quantum advancement.
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Feasibility and Singularity in High-Order Safety-Critical Control for Quadrotor UAVs

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2609.19362v1 Announce Type: cross 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.

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.

Systems and Control (eess.SY); Robotics (cs.RO); Optimization and Control (math.OC)

arXiv:2609.19362 [eess.SY]

(or arXiv:2609.19362v1 [eess.SY] for this version)

https://doi.org/10.48550/arXiv.2609.19362 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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