On-board Sensor Data Monitoring System For Unmanned Aerial Vehicle PHM

2019 ◽  
Author(s):  
Min Jiang ◽  
Benkuan Wang ◽  
Datong Liu ◽  
Yu Peng
Keyword(s):  
Monitoring System ◽  
Unmanned Aerial Vehicle ◽  
Sensor Data ◽  
Data Monitoring ◽  
Aerial Vehicle

Study on Fault Diagnosis of Planetary Gearbox in Unmanned Aerial Vehicle Using Multi sensor Data

2020 ◽  
Vol 20 (4) ◽  
pp. 332-342
Author(s):  
Hyung Jun Park ◽  
Seong Hee Cho ◽  
Kyung-Hwan Jang ◽  
Jin-Woon Seol ◽  
Byung-Gi Kwon ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Unmanned Aerial Vehicle ◽  
Sensor Data ◽  
Planetary Gearbox ◽  
Aerial Vehicle

scholarly journals Smart Environment Monitoring System using Unmanned Aerial Vehicle in Bangladesh

2018 ◽  
pp. 165995
Author(s):  
Md. Al-Farabi ◽  
Muntasir Chowdhury ◽  
Md. Readuzzaman ◽  
Md. Hossain ◽  
Saifur Sabuj ◽  
...  
Keyword(s):  
Monitoring System ◽  
Unmanned Aerial Vehicle ◽  
Smart Environment ◽  
Environment Monitoring ◽  
Aerial Vehicle

scholarly journals Wheat Leaf Area Index Prediction Using Data Fusion Based on High-Resolution Unmanned Aerial Vehicle Imagery

2021 ◽  
Author(s):  
Shuang Wu ◽  
Lei Deng ◽  
Lijie Guo ◽  
Yanjie Wu
Keyword(s):  
High Resolution ◽  
Data Fusion ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Unmanned Aerial Vehicle ◽  
Prediction Accuracy ◽  
Sensor Data ◽  
Support Vector ◽  
Area Index ◽  
Aerial Vehicle

Abstract Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.Methods: To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.Result: The results show that: (1) the soil background reduced the accuracy of the LAI prediction, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data improved LAI prediction accuracy and achieved the best accuracy (R2 = 0.815 and RMSE = 1.023). (3) When compared to other variables, 23 CHM, NRCT, NDRE, and BLUE are crucial for LAI estimation. Even the simple Multiple Linear Regression model could achieve high prediction accuracy (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction.Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.

scholarly journals Real-Time Onboard 3D State Estimation of an Unmanned Aerial Vehicle in Multi-Environments Using Multi-Sensor Data Fusion

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 919 ◽  
Author(s):  
Hao Du ◽  
Wei Wang ◽  
Chaowen Xu ◽  
Ran Xiao ◽  
Changyin Sun
Keyword(s):  
Data Fusion ◽  
State Estimation ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Position Estimation ◽  
Sensor Data ◽  
Measurement Unit ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Aerial Vehicle

The question of how to estimate the state of an unmanned aerial vehicle (UAV) in real time in multi-environments remains a challenge. Although the global navigation satellite system (GNSS) has been widely applied, drones cannot perform position estimation when a GNSS signal is not available or the GNSS is disturbed. In this paper, the problem of state estimation in multi-environments is solved by employing an Extended Kalman Filter (EKF) algorithm to fuse the data from multiple heterogeneous sensors (MHS), including an inertial measurement unit (IMU), a magnetometer, a barometer, a GNSS receiver, an optical flow sensor (OFS), Light Detection and Ranging (LiDAR), and an RGB-D camera. Finally, the robustness and effectiveness of the multi-sensor data fusion system based on the EKF algorithm are verified by field flights in unstructured, indoor, outdoor, and indoor and outdoor transition scenarios.

scholarly journals Analysis of an Unmanned Aerial Vehicle Monitoring System for Resurveying of Shipping Routes

2016 ◽  
Vol 3 (1) ◽  
pp. 102-111
Author(s):  
Aleksandrs Urbahs ◽  
Rima Mickevičienė ◽  
Vasilij Djačkov ◽  
Kristīne Carjova ◽  
Valdas Jankūnas ◽  
...  
Keyword(s):  
Monitoring System ◽  
Unmanned Aerial Vehicle ◽  
Survey Methods ◽  
Environment Monitoring ◽  
Vehicle Monitoring ◽  
Hydrographic Survey ◽  
Survey System ◽  
Aerial Vehicle ◽  
Security Operations ◽  
Marine Safety

Abstract The paper gives brief description of the conventional and innovative hydrography survey methods and constraints connected with the realization. Proposed hydrographic survey system based on the use of Unmanned Aerial and Maritime systems provides functionality to conduct hydrographic measurements and environment monitoring. System can be easily adapted to fulfil marine safety and security operations, e.g. intrusion threat monitoring, hazardous pollutions monitoring and prevention operations, icing conditions monitoring.

scholarly journals Aplikasi UAV (Unmanned Aerial Vehicle) untuk Monitoring Zona Pantai

2021 ◽  
Vol 2 (9) ◽  
pp. 1663-1681
Author(s):  
Dio Mega Putri ◽  
Ahmad Perwira Mulia
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Control Point ◽  
Data Monitoring ◽  
Ground Control ◽  
Ground Control Point ◽  
Area Survey ◽  
Aerial Vehicle

Salah satu fungsi manajemen zona pantai adalah untuk menjaga kestabilan pantai sehingga sangat memerlukan data monitoring zona pantai. Namun, data monitoring dan penelitian tentang kondisi zona pantai dan perubahan garis pantai masih sedikit. Pesatnya perkembangan teknologi mengakibatkan pekerjaan survei dan pemetaan zona pantai kini dapat dilakukan dengan mudah, yaitu dengan menggunakan teknologi UAV. Penelitian ini bertujuan untuk menganalisis kondisi zona pantai berdasarkan ortofoto yang diambil oleh UAV yang dikontrol dengan menggunakan GPS Geodetik di lapangan dan menguraikan tahapan pembentukan fotogrametri dengan UAV hingga menghasilkan gambar ortofoto yang terkoreksi. Metodologi yang diterapkan dalam penelitian ini terdiri dari prasurvei, survei lapangan dan pasca survei. Tahapan awal penelitian meliputi persiapan teknis dan non teknis, pengamatan area survey dan melakukan studi referensi. Tahapan survei lapangan dilakukan untuk mengumpulkan data primer berupa hasil pengukuran Ground Control Point (GCP) dan pengambilan mosaik foto udara menggunakan UAV/Drone, mengambil foto dokumentasi lapangan, serta memenuhi kebutuhan survei lainnya. Tahapan pasca survei merupakan kegiatan pengolahan data foto udara serta pengolahan foto dokumentasi. Nilai ketentuan ketelitian geometri berdasarkan kelas (CE90 dan LE90) termasuk ke dalam kelas 1. Berdasarkan hasil perhitungan selisih jarak beberapa objek di foto pada komputer dan jarak sebenarnya di lapangan, diperoleh rata-rata persentase akurasi sebesar 97%. Hal tersebut menandakan bahwa pengukuran menggunakan UAV memiliki akurasi yang tinggi. UAV merupakan alat yang ideal untuk survei dan pemetaan zona pantai serta masalah pantai lainnya.

scholarly journals Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System

Sensors ◽  
2017 ◽  
Vol 17 (3) ◽  
pp. 502 ◽  
Author(s):  
Jun Ni ◽  
Lili Yao ◽  
Jingchao Zhang ◽  
Weixing Cao ◽  
Yan Zhu ◽  
...  
Keyword(s):  
Monitoring System ◽  
Unmanned Aerial Vehicle ◽  
Crop Growth ◽  
Growth Monitoring ◽  
Aerial Vehicle
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