Visual Navigation and Landing Control of an Unmanned Aerial Vehicle on a Moving Autonomous Surface Vehicle via Adaptive Learning

Zhang, Haitao; Hu, Bin-Bin; Xu, Zhecheng; Lei, Ting; Liu, Bin; Wang, Xudong; Geng, Tao; Zhao, Jin

doi:10.1109/tnnls.2021.3080980

Cited by 43 publications

(21 citation statements)

References 51 publications

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“…The input gate structure is a gate structure that combines the state of historical information and the state of input information to perform calculations, as shown in Equations ( 8) and (9).…”

Section: ∂E ∂Kmentioning

confidence: 99%

“…With the continuous development of sensors and navigation technology, a large number of studies have been conducted on the autonomous navigation methods of quadrotor UAVs, and the development of UAVs toward autonomous navigation and flight has been promoted. Zhang et al [9] designed a multinested autonomous landing algorithm for UAV autonomous landing on unmanned surface craft ASV and conducted experimental research. At the same time, they derived a theory suitable for the stability of UAVs based on the Lyapunov stability theory and conducted experiments on the stability of UAV landings.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Four-Rotor UAV Autonomous Navigation Multisensor Fusion Depth Learning

Liu

et al. 2022

Wireless Communications and Mobile Computing

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Whether it is for military or civilian use, quadrotor UAV has always been one of research central issues. Most of the current quadrotor drones are manually operated and use GPS signals for navigation, which not only limits the operating range of the drone but also consumes a lot of manpower and material resources. This research mainly studies the method of realizing autonomous flight and conflict avoidance of quadrotor UAV by using multisensor system and deep learning method in extreme flight conditions through track prediction. The convolutional neural network method is used to extract the image information collected by the UAV sensor system. And it uses the cyclic neural network to extract the time feature of the information collected by the UAV sensor. The research results show that the track prediction method based on the deep learning method has higher flight accuracy for quadrotor UAVs. The yaw error of the spatial position is only 2.82%, and the maximum error of the time characteristic error tolerance is only 0.77%.

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Section: ∂E ∂Kmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Four-Rotor UAV Autonomous Navigation Multisensor Fusion Depth Learning

Liu

et al. 2022

Wireless Communications and Mobile Computing

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“…For the second challenge of achieving decentralization, Lee et al [13] present an interesting solution to finding a ship and its pose using classical vision algorithms. Zhang et al [14] take a different approach and present a learning-based linear controller that receives inputs from a fiducial marker to land the UAV on a USV that is subject to the waves of a lake. Furthermore, some works also present the application of an MPC controller that enables a flexible-blade helicopter to land on a marine vessel [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Landing a UAV in Harsh Winds and Turbulent Open Waters

M.¹,

Pairet²,

Nascimento³

et al. 2023

IEEE Robot. Autom. Lett.

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Landing an unmanned aerial vehicle (UAV) on top of an unmanned surface vehicle (USV) in harsh open waters is a challenging problem, owing to forces that can damage the UAV due to a severe roll and/or pitch angle of the USV during touchdown. To tackle this, we propose a novel model predictive control (MPC) approach enabling a UAV to land autonomously on a USV in these harsh conditions. The MPC employs a novel objective function and an online decomposition of the oscillatory motion of the vessel to predict, attempt, and accomplish the landing during near-zero tilt of the landing platform. The nonlinear prediction of the motion of the vessel is performed using visual data from an onboard camera. Therefore, the system does not require any communication with the USV or a control station. The proposed method was analyzed in numerous robotics simulations in harsh and extreme conditions and further validated in various real-world scenarios.

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“…1 The PALS uses a vertical rate (H-dot) reference for guidance, the inner-loop is basically a PID controller that specified with vertical rate, acceleration, and pitch rate feedback. [2][3][4] In addition, some other modern control techniques such as backstepping control, 5 sliding mode control, 6 preview control, 7 active disturbance rejection control, 8 intelligent control, 9 and visual servoing control 10 have been applied in ACLS or aircraft flight controller design.…”

Section: Introductionmentioning

confidence: 99%

Barrier Lyapunov function based fixed-time control for automatic carrier landing with disturbances

Zhou

Zheng

Guan³

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part G:

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This paper studies the automatic carrier landing controller design problem with output constraint and fixed-time convergence requirements. The proposed flight controller is separated into guidance, attitude control, and approach power compensation system. In each submodule, universal barrier Lyapunov function and fixed-time control scheme are applied in the controller design. In addition, adaptive estimation method is incorporated to improve the robustness to the external disturbances. The proposed controller ensures the position tracking error, attitude tracking error, and angle of attack tracking error not violate the predesigned boundaries, while the tracking errors will converge to a small set around zero in fixed-time. The output constraint and fixed-time convergence properties of the proposed controller improve the landing security to a narrow flight deck in a limited operation time. The simulation results verify the proposed controller in automatic carrier landing mission.

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Visual Navigation and Landing Control of an Unmanned Aerial Vehicle on a Moving Autonomous Surface Vehicle via Adaptive Learning

Cited by 43 publications

References 51 publications

An Improved Four-Rotor UAV Autonomous Navigation Multisensor Fusion Depth Learning

An Improved Four-Rotor UAV Autonomous Navigation Multisensor Fusion Depth Learning

Landing a UAV in Harsh Winds and Turbulent Open Waters

Barrier Lyapunov function based fixed-time control for automatic carrier landing with disturbances

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