Autonomous Landing of Rotary Wing Unmanned Aerial Vehicles on Underway Ships in a Sea State

2021 ◽  
Vol 104 (1) ◽  
Author(s):  
Jordan Ross ◽  
Mae Seto ◽  
Clifton Johnston
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Sea State ◽  
Autonomous Landing ◽  
Aerial Vehicles ◽  
Rotary Wing

Detection and localization of helipad in autonomous UAV landing: a coupled visual-inertial approach with artificial intelligence

2020 ◽  
Vol 71 (7) ◽  
pp. 828-839
Author(s):  
Thinh Hoang Dinh ◽  
Hieu Le Thi Hong
Keyword(s):  
Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Fiducial Marker ◽  
Localization Algorithm ◽  
Challenging Problem ◽  
Autonomous Landing ◽  
Aerial Vehicles ◽  
Set Up ◽  
Detection And Localization ◽  
Rotary Wing

Autonomous landing of rotary wing type unmanned aerial vehicles is a challenging problem and key to autonomous aerial fleet operation. We propose a method for localizing the UAV around the helipad, that is to estimate the relative position of the helipad with respect to the UAV. This data is highly desirable to design controllers that have robust and consistent control characteristics and can find applications in search – rescue operations. AI-based neural network is set up for helipad detection, followed by optimization by the localization algorithm. The performance of this approach is compared against fiducial marker approach, demonstrating good consensus between two estimations

scholarly journals Detection and localization of helipad in autonomous UAV landing: a coupled visual-inertial approach with artificial intelligence

2020 ◽  
Vol 71 (7) ◽  
pp. 828-839
Author(s):  
Thinh Hoang Dinh ◽  
Hieu Le Thi Hong
Keyword(s):  
Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Fiducial Marker ◽  
Localization Algorithm ◽  
Challenging Problem ◽  
Autonomous Landing ◽  
Aerial Vehicles ◽  
Set Up ◽  
Detection And Localization ◽  
Rotary Wing

Autonomous landing of rotary wing type unmanned aerial vehicles is a challenging problem and key to autonomous aerial fleet operation. We propose a method for localizing the UAV around the helipad, that is to estimate the relative position of the helipad with respect to the UAV. This data is highly desirable to design controllers that have robust and consistent control characteristics and can find applications in search – rescue operations. AI-based neural network is set up for helipad detection, followed by optimization by the localization algorithm. The performance of this approach is compared against fiducial marker approach, demonstrating good consensus between two estimations

scholarly journals Towards fully autonomous landing on moving platforms for rotary Unmanned Aerial Vehicles

2017 ◽  
Author(s):  
Alejandro Rodriguez-Ramos ◽  
Carlos Sampedro ◽  
Hriday Bavle ◽  
Zorana Milosevic ◽  
Alejandro Garcia-Vaquero ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Autonomous Landing ◽  
Moving Platforms ◽  
Aerial Vehicles

A visual protocol for autonomous landing of unmanned aerial vehicles based on fuzzy matching and evolving clustering

2015 ◽  
Author(s):  
Bruno Sielly Jales Costa ◽  
Vitor Rodrigues Greati ◽  
Vinicius Campos Tinoco Ribeiro ◽  
Celso Soares da Silva ◽  
Ivanilson Franca Vieira
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Autonomous Landing ◽  
Aerial Vehicles ◽  
Fuzzy Matching

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

2018 ◽  
Vol 14 (9) ◽  
pp. 155014771880065 ◽  
Author(s):  
Haiwen Yuan ◽  
Changshi Xiao ◽  
Supu Xiu ◽  
Wenqiang Zhan ◽  
Zhenyi Ye ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Feature Detection ◽  
Field Experiments ◽  
Measurement Range ◽  
Autonomous Landing ◽  
Detection Range ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
Vision Based Localization

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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