Detection of Fiducial Marker With Neural Network Compression

Park, Tae Hyoun; Shin, Heejung; Oh, Hyondong

doi:10.5302/j.icros.2023.23.0054

J.ICROS

2023

DOI: 10.5302/j.icros.2023.23.0054

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Detection of Fiducial Marker With Neural Network Compression

Tae Hyoun Park¹,

Heejung Shin²,

Hyondong Oh³

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2024

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This paper proposes a vision-based autonomous landing strategy for vertical take-off and landing (VTOL) UAVs on a moving platform. The approach uses visual measurements to estimate the relative position and speed between the landing pad and the UAV. Challenges arise when the landing pad moves out of the camera’s field of view or when visual measurements become unstable due to excessive UAV maneuvers, leading to degraded landing performance. To address this, the study introduces a novel tracking guidance law that controls the UAV’s position to keep the camera oriented toward the landing pad. An optimal landing guidance law is also developed to minimize attitude angle variations during the approach phase, ensuring stable image acquisition for improved state estimation and maintaining the camera’s field of view. We proposed a state machine-based landing procedure, incorporating landing decisions and go-around logic, enabling full autonomy. The proposed strategy’s performance is validated through flight tests with a multicopter and a fixed-wing VTOL UAV.

show abstract

Vision-Based Optimal Landing Guidance Law for VTOL UAVs on a Moving Platform

Jang,

Cho,

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et al. 2024

Int. J. Aeronaut. Space Sci.

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Detection of Fiducial Marker With Neural Network Compression

Cited by 2 publications

References 0 publications

Vision-Based Optimal Landing Guidance Law for VTOL UAVs on a Moving Platform

Vision-Based Optimal Landing Guidance Law for VTOL UAVs on a Moving Platform

VAE-Based 3D Image Restoration Method for PCB Profilometry

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