Visual Servoing of a Moving Target by an Unmanned Aerial Vehicle

Chen, Ching-Wen; Hung, Hsin-Ai; Yang, Po-Hung; Cheng, Teng-Hu

doi:10.3390/s21175708

Cited by 8 publications

(2 citation statements)

References 45 publications

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“…In [13], a nonlinear predictive controller was effectively employed to produce the desired velocity for an underwater vehicle while adhering to visibility limitations. The same approach was investigated in [14] to develop a tracking controller for UAVs. The application of the model predictive control (MPC) scheme has been observed in the context of a mobile robot [15] and quadrotor [16].…”

Section: Introductionmentioning

confidence: 99%

Quadrotor UAV Dynamic Visual Servoing Based on Differential Flatness Theory

et al. 2023

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In this paper, we propose 2D dynamic visual servoing (Dynamic IBVS), where a quadrotor UAV tries to track a moving target using a single facing-down perspective camera. As an application, we propose the tracking of a car-type vehicle. In this case, data related to the altitude and the lateral angles have no importance for the visual system. Indeed, to perform the tracking, we only need to know the longitudinal displacements (along the x and y axes) and the orientation along the z-axis. However, those data are necessary for the quadrotor’s guidance problem. Thanks to the concept of differential flatness, we demonstrate that if we manage to extract the displacements according to the three axes and the orientation according to the yaw angle (the vertical axis) of the quadrotor, we can control all the other variables of the system. For this, we consider a camera equipped with a vertical stabilizer that keeps it in a vertical position during its movement (a gimbaled camera). Other specialized sensors measure information regarding altitude and lateral angles. In the case of classic 2D visual servoing, the elaboration of the kinematic torsor of the quadrotor in no way guarantees the physical realization of instructions, given that the quadrotor is an under-actuated system. Indeed, the setpoint has a dimension equal to six, while the quadrotor is controlled only by four inputs. In addition, the dynamics of a quadrotor are generally very fast, which requires a high-frequency control law. Furthermore, the complexity of the image processing stage can cause delays in motion control, which can lead to target loss. A new dynamic 2D visual servoing method (Dynamic IBVS) is proposed. This method makes it possible to generate in real time the necessary movements for the quadrotor in order to carry out the tracking of the target (vehicle) using a single point of this target as visual information. This point can represent the center of gravity of the target or any other part of it. A control by flatness has been proposed, which guarantees the controllability of the system and ensures the asymptotic convergence of the generated trajectory in the image plane. Numerical simulations are presented to show the effectiveness of the proposed control strategy.

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Section: Introductionmentioning

confidence: 99%

Quadrotor UAV Dynamic Visual Servoing Based on Differential Flatness Theory

et al. 2023

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“…Over the past two decades, Unmanned Aerial Vehicles (UAVs) have received much attention from both academia and industry. This is mainly due to their broad applications, which include environmental monitoring [1], [2], cultural heritage frames [3], [4], rescuing [5], target tracking [6], pesticide spraying [7] and Value Added Internet of Thing services (VAIOTs) from the sky [8][9] [10]. Quadrotors, otherwise known as a type of UAVs, have some unique and inherent advantages which make them an important research topic in the field of control engineering.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Constrained Model Predictive Control Approach for Unmanned Aerial Vehicles

et al. 2022

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This paper designs and implements a robust and Time-varying Constrained Model Predictive Controller (TCMPC) for the translational and attitude control of a real quadrotor. The proposed approach considers online optimization to find solutions through the hard and soft constraints. All the controller parameters were derived from the experimental test setup and took into consideration the various restrictions and physical constraints associated with the hand-made quadrotor. The proposed controller can possibly linearize and discretize the nonlinear dynamic model of the quadrotor at every sampling time if all constraints and physical restrictions are considered. The performance of the proposed approach was assessed using both

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Geometric tracking control of a multi‐rotor UAV for partially known trajectories

Kumar,

Roy,

Sujit

2024

Intl J Robust & Nonlinear

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This article presents a trajectory‐tracking controller for multi‐rotor unmanned aerial vehicles (UAVs) in scenarios where only the desired position and heading are known without the higher‐order derivatives. The proposed solution modifies the state‐of‐the‐art geometric controller, effectively addressing challenges related to the non‐existence of the desired attitude and ensuring positive total thrust input for all time. We tackle the additional challenge of the non‐availability of the higher derivatives of the trajectory by introducing novel nonlinear filter structures. We formalize theoretically the effect of these filter structures on the system error dynamics. Subsequently, through a rigorous theoretical analysis, we demonstrate that the proposed controller leads to uniformly ultimately bounded system error dynamics. To demonstrate the controller's effectiveness and potential to enhance multi‐rotor performance and reliability in practical applications, we present a simulation study considering two examples with time‐varying trajectories.

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Visual Servoing of a Moving Target by an Unmanned Aerial Vehicle

Cited by 8 publications

References 45 publications

Quadrotor UAV Dynamic Visual Servoing Based on Differential Flatness Theory

Quadrotor UAV Dynamic Visual Servoing Based on Differential Flatness Theory

Design and Implementation of a Constrained Model Predictive Control Approach for Unmanned Aerial Vehicles

Geometric tracking control of a multi‐rotor UAV for partially known trajectories

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