• Title/Summary/Keyword: rotary-wing unmanned aerial vehicle

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Application and Validation of Delay Dependent Parallel Distributed Compensation Controller for Rotary Wing System (회전익 시스템의 시간지연 종속 병렬분산보상제어기 적용과 검증)

  • You, Young-Jin;Choi, Yun-Sung;Jeong, Jin-Seok;Song, Woo-Jin;Kang, Beom-Soo
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.44 no.12
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    • pp.1043-1053
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    • 2016
  • In this paper, the application of Parallel Distributed Compensation (PDC) controller for fixed pitch rotary wing system was studied. For nonlinear modeling, T-S fuzzy model was utilized to advance system control including the tilt type UAV. PDC controller was designed through the Linear Matrix Inequality (LMI). Experiments for determining the applicability and feasibility of PDC were performed using the 1 axis attitude control equipment and simulation. To verify the performance and characteristics of the controller, Mathworks Co. Simulink was used. After then, the PDC controller performance was verified and the results with developed controller using a 1 axis attitude control equipment were compared. Verification of the feasibility of PDC controller for the fixed pitch rotary wing system and identification of the overall performance and improvement analysis was conducted based on the experimental results.

The Use of Unmanned Aerial Vehicle for Monitoring Individuals of Ardeidae Species in Breeding Habitat: A Case study on Natural Monument in Sinjeop-ri, Yeoju, South Korea (백로류 집단번식지의 개체수 모니터링을 위한 무인항공기 활용연구 - 천연기념물 209호 여주 신접리 백로와 왜가리 번식지를 대상으로 -)

  • Park, Hyun-Chul;Kil, Sung-Ho;Seo, Ok-Ha
    • Journal of the Korean Society of Environmental Restoration Technology
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    • v.22 no.1
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    • pp.73-84
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    • 2019
  • In this research, it is a basic study to investigate the population of birds using UAVs. The research area is Ardeidae species(ASP) habitat and has long-term monitoring. The purpose of the study is to compare the ASP populations which analyzed ground observational survey and UAVs imagery. We used DJI's Mavic pro and Phantom4 for this research. Before investigating the population of ASP, we measured the escape distance by the UAVs, and the escape distances of the two UAVs models were statistically significant. Such a result would be different in UAV size and rotor(rotary wing) noise. The population of ASP who analyzed the ground observation and UAVs imagery count differed greatly. In detail, the population(mean) on the ground observation was 174.9, and the UAVs was 247.1 ~ 249.9. As a result of analyzing the UAVs imagery, These results indicate that the lower the UAVs camera altitude, the higher the ASP population, and the lower the UAVs camera altitude, the higher the resolution of the images and the better the reading of the individual of ASP. And we confirmed analyzed images taken at various altitudes, the individuals of ASP was not statistically significant. This is because the resolution of the phantom was superior to that of mavic pro. Our research is fundamental compared to similar studies. However, long-term monitoring for ASP of South Korea's by ground observation is a barrier of the reliability of the monitoring result. We suggested how to use UAVs which can improve long-term monitoring for ASP habitat.

Diurnal Change of Reflectance and Vegetation Index from UAV Image in Clear Day Condition (청천일 무인기 영상의 반사율 및 식생지수 일주기 변화)

  • Lee, Kyung-do;Na, Sang-il;Park, Chan-won;Hong, Suk-young;So, Kyu-ho;Ahn, Ho-yong
    • Korean Journal of Remote Sensing
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    • v.36 no.5_1
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    • pp.735-747
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    • 2020
  • Recent advanced UAV (Unmanned Aerial Vehicle) technology supply new opportunities for estimating crop condition using high resolution imagery. We analyzed the diurnal change of reflectance and NDVI (Normalized Difference Vegetation Index) in UAV imagery for crop monitoring in clear day condition. Multi-spectral images were obtained from a 5-band multi-spectral camera mounted on rotary wing UAV. Reflectance were derived by the direct method using down-welling irradiance measurement. Reflectance using UAV imagery on calibration tarp, concrete and crop experimental sites did not show stable by time and daily reproducible values. But the CV (Coefficient of Variation) of diurnal NDVI on crop experimental sites was less than 5%. As a result of comparing NDVI at the similar time for two day, the daily mean average ratio of error showed a difference of 0.62 to 3.97%. Therefore, it is considered that NDVI using UAV imagery can be used for time series crop monitoring.

Development and Comparative Analysis of Mapping Quality Prediction Technology Using Orientation Parameters Processed in UAV Software (무인기 소프트웨어에서 처리된 표정요소를 이용한 도화품질 예측기술 개발 및 비교분석)

  • Lim, Pyung-Chae;Son, Jonghwan;Kim, Taejung
    • Korean Journal of Remote Sensing
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    • v.35 no.6_1
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    • pp.895-905
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    • 2019
  • Commercial Unmanned Aerial Vehicle (UAV) image processing software products currently used in the industry provides camera calibration information and block bundle adjustment accuracy. However, they provide mapping accuracy achievable out of input UAV images. In this paper, the quality of mapping is calculated by using orientation parameters from UAV image processing software. We apply the orientation parameters to the digital photogrammetric workstation (DPW) for verifying the reliability of the mapping quality calculated. The quality of mapping accuracy was defined as three types of accuracy: Y-parallax, relative model and absolute model accuracy. The Y-parallax is an accuracy capable of determining stereo viewing between stereo pairs. The Relative model accuracy is the relative bundle adjustment accuracy between stereo pairs on the model coordinates system. The absolute model accuracy is the bundle adjustment accuracy on the absolute coordinate system. For the experimental data, we used 723 images of GSD 5 cm obtained from the rotary wing UAV over an urban area and analyzed the accuracy of mapping quality. The quality of the relative model accuracy predicted by the proposed technique and the maximum error observed from the DPW showed precise results with less than 0.11 m. Similarly, the maximum error of the absolute model accuracy predicted by the proposed technique was less than 0.16 m.