DJI phantom frame quadcopter for aerial monitoring of objects
A preliminary study of quadcopter design for natural disaster detection
Keywords:
DJI phantom frame, quadcopter, disaster detection, technologyAbstract
Quadcopter is an unmanned aircraft driven by four motors and can be controlled remotely, making it usable for detecting an object. Current adaptation of the technology is incredibly effectively used for the public interests, especially for monitoring the safety of victims during natural disasters. The purpose of the present study was to design a quadcopter using the DJI Phantom Frame as an initial test for detecting objects at several heights. The present study used the experimental method, in which the quadcopter was designed first and subsequently several tests were performed. In terms of specifications, the designed quadcopter had an X configuration with 2 rotors at the front and 2 rotors at the rear. It used 2-blade propellers of 9 inch in diameter, Inav FC, Dji Phantom Frame, Esc Blheli_s 30a, DJI 920-kv motor, Phantom 4s battery, DJI gimbal, DJI Phantom camera, DJI Phantom remote control, Ublox m8n GPS, 9-inch propellers, Inav software and DJI GO. The quadcopter was subjected to the following tests: the Li-po (lithium polymer) battery power tests; quadcopter remote control test, Global Position System (GPS) test, and radio telemetry test.
Downloads
References
Al-Emadi, S., Al-Ali, A., & Al-Ali, A. (2021). Audio-based drone detection and identification using deep learning techniques with dataset enhancement through generative adversarial networks†. Sensors, 21(15), 1–26.
Allison, S., Bai, H., & Jayaraman, B. (2020). Wind estimation using quadcopter motion: A machine learning approach. Aerospace Science and Technology, 98, 105699. https://doi.org/10.1016/j.ast.2020.105699
Anbarasan, M., Muthu, B., Sivaparthipan, C. B., Sundarasekar, R., Kadry, S., Krishnamoorthy, S., & Dasel, A. A. (2020). Detection of flood disaster system based on IoT, big data and convolutional deep neural network. Computer Communications, 150, 150-157. https://doi.org/10.1016/j.comcom.2019.11.022
Andika, I. G., Yanti, C. P., & Cardewa, M. (2018). Quadcopter Obstacle Avoidance Dengan Sensor Inframerah Untuk Pemantauan Bencana Alam Melalui Udara. Jurnal Pendidikan Teknologi Dan Kejuruan, 15(1), 71–80.
Arnanto, A., Wulan Mei, E. T., Hizbaron, D. R., & Utami, W. (2019). Pesawat Udara Nir Awak (Uav) Untuk Penyediaan Data Spasial Bidang Tanah Di Kawasan Rawan Bencana. BHUMI: Jurnal Agraria Dan Pertanahan, 5(2), 271–281.
Coluccia, A., Fascista, A., Schumann, A., Sommer, L., Dimou, A., Zarpalas, D., Méndez, M., de la Iglesia, D., González, I., Mercier, J. P., Gagné, G., Mitra, A., & Rajashekar, S. (2021). Drone vs. Bird detection: Deep learning algorithms and results from a grand challenge. Sensors, 21(8), 1–27.
Faradila, N., Nugroho, S. P., & Sumari, A. D. W. (2016). Efektivitas Pemanfaatan Wahana Tanpa Awak Dalam Peliputan dan Penanganan Bencana. Jurnal Dialog dan Penanggulangan Bencana, 7(1), 56-70.
Harista, A. F., & Nuryadi, S. (2018). Sistem Navigasi Quadcopter dan Pemantauan Udara. JURNAL TeknoSAINS Seri Teknik Elektro, 01(01), 1–22.
Khan, A., Gupta, S., & Gupta, S. K. (2020). Multi-hazard disaster studies: Monitoring, detection, recovery, and management, based on emerging technologies and optimal techniques. International journal of disaster risk reduction, 47, 101642. https://doi.org/10.1016/j.ijdrr.2020.101642
Komilova, N. K., Rakhimova, T., Allaberdiev, R. K., Mirzaeva, G. S., & Egamberdiyeva, U. T. (2021). Ecological situation: the role of education and spirituality in improving health of population. International Journal of Health Sciences, 5(3), 302–312. https://doi.org/10.53730/ijhs.v5n3.1512
Kushayati, N. (2014). Analisis Metode Triage Prehospital pada Insiden Korban Masal (Mass Casualty Incident). Jurnal Ilmiah WUNY, 16(4), 1–9.
Menggala, G. P. (2018). Autonomous System Pada Quadcopter Pencari Korban Bencana Banjir. Departemen Tehnik Elektro Otomasi ITS, 1(3), 1–54.
Pawełczyk, M., & Wojtyra, M. (2020). Real world object detection dataset for quadcopter unmanned aerial vehicle detection. IEEE Access, 8, 174394–174409.
Paz, C., Suárez, E., Gil, C., & Baker, C. (2020). CFD analysis of the aerodynamic effects on the stability of the flight of a quadcopter UAV in the proximity of walls and ground. Journal of Wind Engineering and Industrial Aerodynamics, 206, 104378. https://doi.org/10.1016/j.jweia.2020.104378
Pi, Y., Nath, N. D., & Behzadan, A. H. (2020). Convolutional neural networks for object detection in aerial imagery for disaster response and recovery. Advanced Engineering Informatics, 43, 101009. https://doi.org/10.1016/j.aei.2019.101009
Rafiq, A. A., Riyanto, S. D., Aprilas, B. D., & Pratama, R. P. (2020). Image Processing untuk Deteksi Objek pada Daerah Bencana. INVOTEK: Jurnal Inovasi Vokasional Dan Teknologi, 20(2), 9–18.
Selwyn, N. (2003). Apart from technology: understanding people’s non-use of information and communication technologies in everyday life. Technology in society, 25(1), 99-116. https://doi.org/10.1016/S0160-791X(02)00062-3
Swamardika, I. A., Setiawan, I. N., & Budiastra, I. N. (2014). Rancang Bangun Quadcopter Robot Sebagai Alat Pemantau Jarak Jauh Kawasan Lingkungan Bencana. In Seminar Nasional Sains dan Teknologi.
Thostenson, E. T., Ren, Z., & Chou, T. W. (2001). Advances in the science and technology of carbon nanotubes and their composites: a review. Composites science and technology, 61(13), 1899-1912. https://doi.org/10.1016/S0266-3538(01)00094-X
Ullah, S., Khan, Q., Mehmood, A., Kirmani, S. A. M., & Mechali, O. (2022). Neuro-adaptive fast integral terminal sliding mode control design with variable gain robust exact differentiator for under-actuated quadcopter UAV. ISA transactions, 120, 293-304. https://doi.org/10.1016/j.isatra.2021.02.045
Wang, Y., Wang, W., Zhou, M., Ren, A., & Tian, Z. (2020). Remote monitoring of human vital signs based on 77-GHZ MM-WAVE FMCW radar. Sensors (Switzerland), 20(10), 1–23.
Xu, B., Wang, W., Falzon, G., Kwan, P., Guo, L., Chen, G., ... & Schneider, D. (2020). Automated cattle counting using Mask R-CNN in quadcopter vision system. Computers and Electronics in Agriculture, 171, 105300. https://doi.org/10.1016/j.compag.2020.105300
Xu, X., Peng, S., & Yang, F. (2018). Development of a ground penetrating radar system for large-depth disaster detection in coal mine. Journal of Applied Geophysics, 158, 41-47. https://doi.org/10.1016/j.jappgeo.2018.07.006
Published
How to Cite
Issue
Section
Copyright (c) 2022 International journal of health sciences
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Articles published in the International Journal of Health Sciences (IJHS) are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant IJHS right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.
Articles published in IJHS can be copied, communicated and shared in their published form for non-commercial purposes provided full attribution is given to the author and the journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
This copyright notice applies to articles published in IJHS volumes 4 onwards. Please read about the copyright notices for previous volumes under Journal History.