Abrupt total rotor failures can invalidate nominal allocation despite remaining wrench capability. This paper develops an allocator-layer passive fault-tolerant control (AL-PFTC) scheme for overactuated vector-thrust aerial vehicles. Analysis of convexified attainable force and torque spaces at horizontal hover compares biaxial- and uniaxial-tilt configurations and screens fault cases using prescribed reserve thresholds. Instantaneous wrench loss relative to these reserves characterizes the initial redistribution burden. AL-PFTC adapts the allocation matrix using a virtual error derived from filtered wrench commands and an onboard external wrench estimate, with adaptive parameter bounds constructed from right inverses. Joint analysis including estimator residuals and filter dynamics establishes local uniform ultimate boundedness for position and attitude regulation under the stated conditions. Controller-layer passive fault-tolerant control (CL-PFTC) provides a reference with nominal allocation and observer compensation. Both share a nominal tracking baseline and run at 600 Hz on an STM32H753 without explicit fault identification, fault-mode switching, or online optimization. Comparisons show that AL-PFTC satisfies the recovery criterion in more tested hovering cases than CL-PFTC. Additional experiments using only onboard sensing, including indoor tracking under wind disturbance, outdoor tracking under wind and low-temperature conditions, narrow-frame traversal, and contact-based aerial writing, further validate the robustness of AL-PFTC in complex operational environments.
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.
Autonomous fall recovery is a critical capability for quadrotors operating in real-world environments, where collisions or failures may leave the vehicle resting on the ground in an arbitrary attitude. This problem is challenging because recovery must be achieved under limited onboard sensing, in constrained free space, with ground contact, and in the presence of unknown disturbances. In this letter, we present an RL-based framework for autonomous fall recovery of a quadrotor from arbitrary ground attitudes to stable hover using only lightweight onboard sensors. To address severe partial observability and intermittent sensor invalidity, we train a recurrent policy within an asymmetric actor--critic architecture, leveraging an Incremental Nonlinear Dynamic Inversion (INDI) controller to track the policy output. Combined with high-fidelity simulations of motor response and optical flow, the overall training framework significantly reduces the sim-to-real gap. Simulation ablation studies validate the importance of the main design choices, while real-world experiments demonstrate zero-shot transfer and robust recovery under different initial attitudes, wind disturbances, and additional payloads. These results demonstrate that agile quadrotor fall recovery can be achieved without explicit state estimation using only limited and unreliable onboard sensing.
This work demonstrates experimentally the existence of a hexarotor prototype, termed Opti-Hexa, that simultaneously achieves practical resilience to single-propeller failures and energy efficiency comparable to a standard Star-shaped prototype with the same size, weight, hardware and software. Leveraging a novel open-source morphing platform, we investigate the trade-offs across a continuous range of geometries by varying the angles between adjacent propellers. We study practical efficiency through a data-fitted empirical power model and evaluate practical resilience by comparing the position accuracy and rotational kinetic energy during failure to those observed under nominal hovering conditions. Our experiments confirm the existence of a geometric viability region for this specific morphing platform, where resilience is ensured without the aerodynamic efficiency losses typically associated with practically resilient designs found in the state of the art. The complete hardware and software of the morphing platform are released to support further research.