Neural networks to aid the autonomous landing of a UAV on a ship

Moriarty, Padraic; Sheehy, Robert; Doody, Pat

doi:10.1109/issc.2017.7983613

Cited by 13 publications

(6 citation statements)

References 4 publications

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“…[8][9][10] Based on speeded-up robust features (SURF) feature descriptors and fast approximate nearest neighbor search (FLANN) matcher, a template matching method 11 was presented to determine the relative position of the landing target. Also, artificial neural networks (ANN) have been employed to estimate the state of the landing UAV in Moriarty et al 12 Similar to our work, Araar et al 13 have designed an adequate pad to extend the detection range. The proposed pad is composed of patterns of different sizes, permitting their detection from high as well as very low altitudes.…”

Section: Previous Workmentioning

confidence: 70%

A hierarchical vision-based localization of rotor unmanned aerial vehicles for autonomous landing

Yuan

Xiao

Xiu

et al. 2018

International Journal of Distributed Sensor Networks

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The vision-based localization of rotor unmanned aerial vehicles for autonomous landing is challenging because of the limited detection range. In this article, to extend the vision detection and measurement range, a hierarchical vision-based localization method is proposed for unmanned aerial vehicle autonomous landing. In such a hierarchical framework, the landing is defined into three phases: “Approaching,”“Adjustment,” and “Touchdown,” in which visual artificial features at different scales can be detected from the designed object pattern for unmanned aerial vehicle pose recovery. The corresponding feature detection and pose estimation algorithms are also presented. In the end, typical simulation and field experiments have been carried out to illustrate the proposed method. The results show that our hierarchical vision-based localization has the ability to a consecutive unmanned aerial vehicle localization in a wider working range from far to near, which is significant for autonomous landing.

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Section: Previous Workmentioning

confidence: 70%

A hierarchical vision-based localization of rotor unmanned aerial vehicles for autonomous landing

Yuan

Xiao

Xiu

et al. 2018

International Journal of Distributed Sensor Networks

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“…Landing a micro aerial vehicle (MAV) autonomously on a moving platform in challenging conditions is a problem of interest for applications that require collaboration of MAVs and ships [1,2] or ground vehicles, such as truck-drone delivery systems [3,4]. Under these circumstances, precise control robust to disturbances is of utmost importance to ensure the success of the mission.…”

Section: Introductionmentioning

confidence: 99%

Autonomous MAV Landing on a Moving Platform with Estimation of Unknown Turbulent Wind Conditions

Paris

Tagliabue

How

2021

AIAA Scitech 2021 Forum

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This paper presents an autonomous landing of a micro aerial vehicle (MAV) on a moving platform immersed in turbulent wind conditions. We estimate the 3D wind vector acting on the vehicle using a model-based and a deep learning-based approach. A disturbance-aware boundary layer sliding controller then uses this estimation to generate a control input that provides trajectory tracking guarantees in the presence of unknown, but bounded disturbances. The approach presented integrates our previous works on control and estimation, and we show its performance in a challenging setting. The experiments show that our methods enable fast landing on a moving platform in turbulent, unknown wind conditions.

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“…Wang Liyang and other scholars [15] proposed a drone autonomous landing system based on airborne monocular vision, which can improve the ability of autonomous tracking and landing for drones under simulated sea conditions. Moriarty, P et al [16] provided an artificial neural network-based autonomous landing method for UAVs. Using the data generated by simulating the sea motion, the current relative azimuth and distance of the UAV to the landing platform were calculated, and the coordinate pairs were calculated, which normalized processing for training neural networks.…”

Section: Introductionmentioning

confidence: 99%

Research on Optimal Landing Trajectory Planning Method between an UAV and a Moving Vessel

Tan

Yang

et al. 2019

Applied Sciences

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The location, velocity, and flight path angle of an autonomous unmanned aerial vehicle (UAV) landing on a moving vessel are key factors for an optimal landing trajectory. To tackle this challenge, this paper proposes a method for calculating the optimal approach landing trajectory between an UAV and a small vessel. A numerical approach (iterative method) is used to calculate the optimal approach landing trajectory, and the initial lead is introduced in the calculation process of the UAV trajectory for the inclination and heading angle for accuracy improvement, so that the UAV can track and calculate the optimal landing trajectory with high precision. Compared with the variational method, the proposed method can calculate an optimal turning direction angle for the UAV during the landing. Simulation experiments verify the effectiveness of the proposed algorithm and give optimal initialization values.

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Neural networks to aid the autonomous landing of a UAV on a ship

Cited by 13 publications

References 4 publications

A hierarchical vision-based localization of rotor unmanned aerial vehicles for autonomous landing

A hierarchical vision-based localization of rotor unmanned aerial vehicles for autonomous landing

Autonomous MAV Landing on a Moving Platform with Estimation of Unknown Turbulent Wind Conditions

Research on Optimal Landing Trajectory Planning Method between an UAV and a Moving Vessel

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