Vision-based Autonomous Landing of Unmanned Aerial Vehicle on a Motional Unmanned Surface Vessel

Xu, Zhecheng; Hu, Bin-Bin; Liu, Bin; Xd, Wang; Zhang, Haitao

doi:10.23919/ccc50068.2020.9188979

Cited by 15 publications

(11 citation statements)

References 16 publications

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“…Notice that the precision of the vision system was validated via VICON ground truth by an emulated deck motion under sea-state 6, but the average distance error of landing was relatively large compared with the size of the landing pad. The work of Xu et al [ 39 ] customized a nested AprilTag on a moving vessel as the landing marker to assist UAV autonomous landing, in which the hierarchical marker design along with a three-stage marker detection scheme may suit the vision system at different altitude levels. Although a successful landing attempt was reported during the field tests, more details, such as the accuracy of the estimated relative pose and the speed of the moving vessel, were yet to be revealed.…”

Section: Literature Reviewmentioning

confidence: 99%

Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments

Lin

Jin

Chen

2021

Sensors

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Landing an unmanned aerial vehicle (UAV) autonomously and safely is a challenging task. Although the existing approaches have resolved the problem of precise landing by identifying a specific landing marker using the UAV’s onboard vision system, the vast majority of these works are conducted in either daytime or well-illuminated laboratory environments. In contrast, very few researchers have investigated the possibility of landing in low-illumination conditions by employing various active light sources to lighten the markers. In this paper, a novel vision system design is proposed to tackle UAV landing in outdoor extreme low-illumination environments without the need to apply an active light source to the marker. We use a model-based enhancement scheme to improve the quality and brightness of the onboard captured images, then present a hierarchical-based method consisting of a decision tree with an associated light-weight convolutional neural network (CNN) for coarse-to-fine landing marker localization, where the key information of the marker is extracted and reserved for post-processing, such as pose estimation and landing control. Extensive evaluations have been conducted to demonstrate the robustness, accuracy, and real-time performance of the proposed vision system. Field experiments across a variety of outdoor nighttime scenarios with an average luminance of 5 lx at the marker locations have proven the feasibility and practicability of the system.

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Section: Literature Reviewmentioning

confidence: 99%

Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments

Lin

Jin

Chen

2021

Sensors

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“…Black and white patterns such as Apriltags [63] and Quick Response (QR) codes [64] are also used as markers. Mengyin Fu et al [64] uses a mixed marker with a QR code inside of a circle.…”

Section: Algorithms For Uav Precision Landingmentioning

confidence: 99%

Precision Landing for Low-Maintenance Remote Operations with UAVs

Moreira¹,

Azevedo

Ferreira³

et al. 2021

Drones

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This work proposes a fully integrated ecosystem composed of three main components with a complex goal: to implement an autonomous system with a UAV requiring little to no maintenance and capable of flying autonomously. For this goal, was developed an autonomous UAV, an online platform capable of its management and a landing platform to enclose and charge the UAV after flights. Furthermore, a precision landing algorithm ensures no need for human intervention for long-term operations.

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“…More recently, researchers have begun testing their approaches through real-world experiments. For example, Xu et al [12] use a fiducial marker for a decentralized approach, so as to follow the USV and use a PD controller for landing once the USV is discovered. 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.…”

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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Vision-based Autonomous Landing of Unmanned Aerial Vehicle on a Motional Unmanned Surface Vessel

Cited by 15 publications

References 16 publications

Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments

Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments

Precision Landing for Low-Maintenance Remote Operations with UAVs

Landing a UAV in Harsh Winds and Turbulent Open Waters

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