Control Design and Visual Autonomous Navigation of Quadrotor

Mouaad, Boughellaba; Razika, Boushaki Zamoum; Ramzi, Rabah Hazila; Cherifi, Karim

doi:10.1109/icaee47123.2019.9014735

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…A comparison between SLAM algorithms for mobile robot navigation in indoor environments is reported in [ 11 ], where it is concluded that ORB-SLAM can be used to determine the robot position with an additional module to recover the scale factor. The report [ 12 ] presents quadrotor autonomous navigation using a SLAM algorithm without determining the position scale factor.…”

Section: Introductionmentioning

confidence: 99%

Scale Factor Estimation for Quadrotor Monocular-Vision Positioning Algorithms

Gómez-Casasola

Rodriguez-Cortes

2022

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

Scale Factor Estimation for Quadrotor Monocular-Vision Positioning Algorithms

Gómez-Casasola

Rodriguez-Cortes

2022

Sensors

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“…Ref. 21 presents many linear controllers to stabilize a quadrotor. The results were experimentally validated using an AR.Drone 2.0 and monocular vision.…”

Section: Introductionmentioning

confidence: 99%

Real-time robust tracking control for a quadrotor using monocular vision

Montoya-Morales

Guerrero-Sánchez

Valencia‐Palomo

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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In this paper, the autonomous trajectory-tracking problem for an Unmanned Aerial Vehicle (UAV) based on Sliding Mode Control (SMC) algorithms is treated. The control system is implemented in real-time to stabilize a commercial AR.Drone 2.0 quadrotor using monocular vision. The under-actuated mathematical model is based on the Newton-Euler formulation. The algorithm allows the stabilization of the quadrotor in all its states under the simultaneous effect of parametric uncertainties and constant external disturbances. The vision algorithm uses a monocular camera to estimate the vehicle’s position. The experimental test results and numerical simulations show the effectiveness and robustness of the proposed controller.

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Control Design and Visual Autonomous Navigation of Quadrotor

Cited by 2 publications

References 8 publications

Scale Factor Estimation for Quadrotor Monocular-Vision Positioning Algorithms

Scale Factor Estimation for Quadrotor Monocular-Vision Positioning Algorithms

Real-time robust tracking control for a quadrotor using monocular vision

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