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The growing use of Unmanned Aerial Vehicles (UAVs) raises the need to improve their autonomous navigation capabilities. Visual odometry allows for dispensing positioning systems, such as GPS, especially on indoor flights. This paper reports an effort toward UAV autonomous navigation by proposing a translational velocity observer based on inertial and visual measurements for a quadrotor. The proposed observer complementarily fuses available measurements from different domains and is synthesized following the Immersion and Invariance observer design technique. A formal Lyapunov-based observer error convergence to zero is provided. The proposed observer algorithm is evaluated using numerical simulations in the Parrot Mambo Minidrone App from Simulink-Matlab.

This paper introduces a new decentralized control strategy for an unmanned aerial manipulator (UAM) constrained to the vertical plane. The control strategy comprises two loops: the first compensates for the aerial vehicle's impact on the manipulator; and the second one implements independent controllers for the aerial vehicle and the manipulator. The controller for the aerial vehicle includes an estimator to compensate for the dynamic influence of the manipulator, even if it is affected by external wind-gust disturbances. The manipulator has two revolute joints; however, it is modeled as an dynamically equivalent manipulator, with one revolute and one prismatic joint. The proposed control strategy's performance is evaluated using a simulator that includes the vehicle's aerodynamics and the manipulator's contact force and moment., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Tlatelpa-Osorio et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This note presents a new passivity-based controller that ensures asymptotic stability for quadrotor position without solving partial differential equations or performing a partial dynamic inversion. After a resourceful change of coordinates, a pre-feedback controller, and a backstepping stage on the yaw angle dynamic, it is possible to identify new quadrotor cyclo passive outputs. Then, a simple proportional-integral controller of these cyclo-passive outputs completes the design. The cyclo-passive outputs allow the construction of an energy-based Lyapunov function that includes five out of six quadrotor degrees of freedom and guarantees asymptotic stability of the desired equilibrium. Moreover, the constant velocity reference tracking problem is solved with a slight modification to the proposed controller. Finally, the approach is validated through simulations and real-time experimental results., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 ISA. Published by Elsevier Ltd. All rights reserved.)

Unmanned aerial vehicle (UAV) autonomous navigation requires access to translational and rotational positions and velocities. Since there is no single sensor to measure all UAV states, it is necessary to fuse information from multiple sensors. This paper proposes a deterministic estimator to reconstruct the scale factor of the position determined by a simultaneous localization and mapping (SLAM) algorithm onboard a quadrotor UAV. The position scale factor is unknown when the SLAM algorithm relies on the information from a monocular camera. Only onboard sensor measurements can feed the estimator; thus, a deterministic observer is designed to rebuild the quadrotor translational velocity. The estimator and the observer are designed following the immersion and invariance method and use inertial and visual measurements. Lyapunov's arguments prove the asymptotic convergence of observer and estimator errors to zero. The proposed estimator's and observer's performance is validated through numerical simulations using a physics-based simulator.