ORIGINAL ARTICLE
Recommendations for planning UAV flight missions for geodata collection
1
Educational and Scientific Institute of Biology, Chemistry and Bioresources, Department of Geomatics, Land and Agricultural Management, Chernivtsi National University, St. Lesya Ukrainka, 25, Chernivtsi, 58012, Ukraine
2
Department of Geodesy and Land Management, Ivano-Frankivsk National Technical University of Oil and Gas, 15 Karpatska Str., Ivano-Frankivsk, 76019, Ukraine
3
Faculty of Aircraft Management Systems, Department of Mechatronics and Electrical Engineering, Mykola Yehorovich Zhukovsky National Aerospace University "KHAI", St. Vadym Manko, 17, Kharkiv, 61070, Ukraine
4
Institute of Physical, Technical and Computer Sciences, Department of Radio Engineering and Information Security, Chernivtsi National University, St. Storozhynetska, 101, Chernivtsi, 58004, Ukraine
These authors had equal contribution to this work
A - Research concept and design; B - Collection and/or assembly of data; C - Data analysis and interpretation; D - Writing the article; E - Critical revision of the article; F - Final approval of article
Submission date: 2024-10-30
Final revision date: 2025-01-08
Acceptance date: 2025-02-17
Publication date: 2025-03-14
Corresponding author
Taras Hutsul
Educational and Scientific Institute of Biology, Chemistry and Bioresources, Department of Geomatics, Land and Agricultural Management, Chernivtsi National University, St. Lesya Ukrainka, 25, Chernivtsi, 58012, Ukraine
Educational and Scientific Institute of Biology, Chemistry and Bioresources, Department of Geomatics, Land and Agricultural Management, Chernivtsi National University, St. Lesya Ukrainka, 25, Chernivtsi, 58012, Ukraine
Reports on Geodesy and Geoinformatics 2025;119:62-70
KEYWORDS
TOPICS
ABSTRACT
A number of different types of information are generally associated with places. It is estimated that about 75-90 % of information may contain an official link to a specific area, expressed as, for example, coordinates, or addresses, and therefore has a spatial character, making data collection a responsible and important stage, which reasonably affects the quality of its results. Information and its sources are treated with particular care and rigor in the scientific field: in most cases, the data must be relevant, reliable, technically simple, and collected quickly at reasonable costs. The analysis of geographic information makes it possible to obtain qualitatively new information and reveal previously unknown patterns. Modern data collection methods are divided into three distinct groups: terrestrial, cartographic, and remote. Remote or aerospace methods are considered to be those that allow information to be collected. It refers to objects on the Earth's surface, phenomena, or processes from space or the atmosphere, recorded by detecting electromagnetic radiation on the ground across various spectral ranges. The involvement of various platforms (providers) of surveillance equipment makes it possible to divide them into: space, aerial photography, and images from Unmanned Aerial Vehicles (UAVs). As a technology justified on security grounds, UAVs show great promise in many areas of application. Effective planning of drone missions allows for the collection of larger sets of data with a higher level of detail and in a shorter period of time. The continuity of information collection for a given territory allows for the most accurate and reliable three-dimensional modelling, spatial analysis and geostatistics of the local situation.
REFERENCES (64)
1.
Aggarwal Shubhani, Kumar Neeraj. (2020). Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges. Computer Communications. 149: 270-299. doi:10.1016/j.comcom.2019.10.014.
2.
Alekseev VE, Talanov VA. (2005). Chapter 3.4. Finding the shortest paths in a graph. Graphs. Computation Models. Data structures. Nizhny Novgorod: Publishing House of the Nizhny Novgorod State. University. 236: 237-237.
3.
Alyassi Rashid, Khonji Majid, Karapetyan Areg, Chau Sid Chi-Kin, Elbassioni Khaled, Tseng Chien-Ming. (2023). Autonomous Recharging and Flight Mission Planning for Battery-Operated Autonomous Drones. IEEE Transactions on Automation Science and Engineering. 20 (2): 1034-1046. doi:10.1109/tase.2022.3175565.
4.
Apollo Michal, Jakubiak Mateusz, Nistor Sorin, Lewinska Paulina, Krawczyk Artur, Borowski Lukasz, Specht Mariusz, Krzykowska-Piotrowska Karolina, Marchel Lukasz, Pęska-Siwik Agnieszka, Kardoš Miroslav, Maciuk Kamil. (2023). GEODATA IN SCIENCE – A REVIEW OF SELECTED SCIENTIFIC FIELDS. Acta Scientiarum Polonorum Formatio Circumiectus. 22 (2): 17-40. doi:10.15576/asp.fc/2023.22.2.02.
5.
(2021). Copter Documentation, ArduPilot Dev Team. https://ardupilot.org/copter/index.html, Accessed 23 June 2024.
6.
(2021). Mission Planner Overview, ArduPilot Dev Team. https://ardupilot.org/planner/docs/mission-planneroverview.html.
7.
Barnawi Ahmed, Kumar Krishan, Kumar Neeraj, Thakur Nisha, Alzahrani B, er, Almansour Amal. (2023). Unmanned Ariel Vehicle (UAV) Path Planning for Area Segmentation in Intelligent Landmine Detection Systems. Sensors. 23 (16): 7264-7264. doi:10.3390/s23167264.
8.
Bellman Richard. (1958). On a routing problem. Quarterly of Applied Mathematics. 16 (1): 87-90.
9.
Berra E. F., Peppa M. V. (2020). ADVANCES AND CHALLENGES OF UAV SFM MVS PHOTOGRAMMETRY AND REMOTE SENSING: SHORT REVIEW. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XLII-3/W12-2020: 267-272. doi:10.5194/isprs-archives-xlii-3-w12-2020-267-2020.
10.
Budiyono Agus, Higashino Shin-Ichiro. (2023). A Review of The Latest Innovations in UAV Technology. doi:10.5281/ZENODO.8062292.
11.
Burrough Peter A, McDonnell Rachael A, Lloyd Christopher D. (2015). Principles of geographical information systems. Oxford university press.
12.
Chintanadilok Jesse, Patel Sahaj, Zhuang Yilin, Singh Aditya. (2022). Mission Planner: An Open-Source Alternative to Commercial Flight Planning Software for Unmanned Aerial Systems: AE576/AE576, 8/2022. EDIS. 2022 (4). doi:10.32473/edis-ae576-2022.
13.
Choi Hyoung Sik, Lee Sangjong, Ryu Hyeok, Shim Hyunchul, Ha Cheolkeun. (2015). Dynamics and Simulation of the Effects of Wind on UAVs and Airborne Wind Measurement. Transactions Of The Japan Society For Aeronautical And Space Sciences. 58 (4): 187-192. doi:10.2322/tjsass.58.187.
14.
Cormen Thomas H, Leiserson CE, Rivest RL, Stein C. (2009). Introduction to Algorithms, Third Edition. Massachusetts London, England.
15.
Dangermond Jack, Goodchild Michael F. (2019). Building geospatial infrastructure. Geo-spatial Information Science. 23 (1): 1-9. doi:10.1080/10095020.2019.1698274.
16.
Didulescu Daniel, Buciu Claudiu, Răducanu Dan-Gabriel. (2018). Mission Planner for GEOINT Data Acquisition. Journal of Military Technology. 1 (1): 51-56. doi:10.32754/jmt.2018.1.09.
17.
Dijkstra E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik. 1 (1): 269-271.
18.
(2023). EASA: 20 years of safe aviation. EASA European Aviation Safety Agency, https://www.easa.europa.eu/en/regulations.
19.
Ebeid Emad, Skriver Martin, Terkildsen Kristian Husum, Jensen Kjeld, Schultz Ulrik Pagh. (2018). A survey of Open-Source UAV flight controllers and flight simulators. Microprocessors and Microsystems. 61: 11-20. doi:10.1016/j.micpro.2018.05.002.
20.
Ekaso Desta, Nex Francesco, Kerle Norman. (2020). Accuracy assessment of real-time kinematics (RTK) measurements on unmanned aerial vehicles (UAV) for direct geo-referencing. Geo-spatial Information Science. 23 (2): 165-181. doi:10.1080/10095020.2019.1710437.
21.
Elmokadem Taha, Savkin Andrey V. (2021). Towards Fully Autonomous UAVs: A Survey. Sensors. 21 (18): 6223-6223. doi:10.3390/s21186223.
22.
Elwood Sarah, Goodchild Michael F., Sui Daniel Z. (2012). Researching Volunteered Geographic Information: Spatial Data, Geographic Research, and New Social Practice. Annals of the Association of American Geographers. 102 (3): 571-590. doi:10.1080/00045608.2011.595657.
23.
Garg P K. (2020). Digital land surveying and mapping. New Age International Pvt Ltd.
24.
(2015). A guide to the role of Standards in Geospatial Information Management. Prepared cooperatively by the: Open Geospatial Consortium (OGC), The International Organization for Standards (ISO), Technical Committee 211 Geographic information/Geomatics, International Hydrographic Organization (IHO).
25.
Gómez-López José Miguel, Pérez-García José Luis, Mozas-Calvache Antonio Tomás, Delgado-García Jorge. (2020). Mission Flight Planning of RPAS for Photogrammetric Studies in Complex Scenes. ISPRS International Journal of Geo-Information. 9 (6): 392-392. doi:10.3390/ijgi9060392.
26.
Goodchild Michael F. (2005). Geographic Information Systems. In: Encyclopedia of Social Measurement. 107-113. Elsevier. doi:10.1016/b0-12-369398-5/00335-2.
27.
Hart Peter, Nilsson Nils, Raphael Bertram. (1968). A Formal Basis for the Heuristic Determination of Minimum Cost Paths. IEEE Transactions on Systems Science and Cybernetics. 4 (2): 100-107.
28.
Henderson Isaac Levi. (2022). Aviation safety regulations for unmanned aircraft operations: Perspectives from users. Transport Policy. 125: 192-206. doi:10.1016/j.tranpol.2022.06.006.
29.
Hentati Aicha Idriss, Krichen Lobna, Fourati Mohamed, Fourati Lamia Chaari. (2018). Simulation Tools, Environments and Frameworks for UAV Systems Performance Analysis. 2018 14th International Wireless Communications and Mobile Computing Conference (IWCMC). 1495-1500. doi:10.1109/iwcmc.2018.8450505.
30.
Ho Florence, Geraldes Ruben, Goncalves Artur, Rigault Bastien, Sportich Benjamin, Kubo Daisuke, Cavazza Marc, Prendinger Helmut. (2022). Decentralized Multi-Agent Path Finding for UAV Traffic Management. IEEE Transactions on Intelligent Transportation Systems. 23 (2): 997-1008. doi:10.1109/tits.2020.3019397.
31.
(2016). Huawei TSMC [advertisement], 2016 IEEE Electrical Design of Advanced Packaging and Systems (EDAPS). doi:10.1109/EDAPS.2016.7893107.
32.
Hutsul Taras, Zhezhera Ivan, Tkach Vladyslav. (2022). FEATURES OF UAV CLASSIFICATION AND SELECTION METHODS. Technical Sciences and Technologies. (4(30)): 201-212. doi:10.25140/2411-5363-2022-4(30)-201-212.
33.
Hutsul Taras, Karpinskyi Yurii. (2021). Possibility of applying geoinformation multiagent optimisation for planning the development of road networks. Reports on Geodesy and Geoinformatics. 112 (1): 1-8. doi:10.2478/rgg-2021-0002.
34.
Hutsul Taras, Smirnov Yaroslav. (2017). COMPARATIVE ACCURACY ASSESSMENT OF GLOBAL DTM AND DTM GENERATED FROM SOVIET TOPOGRAPHIC MAPS FOR THE PURPOSES OF ROAD PLANNING. Geodesy and cartography. 43 (4): 173-181. doi:10.3846/20296991.2017.1412638.
35.
Hutsul T. (2019). Geoinformation Multiagent Optimization of Road Transport Network Development Planning (Based on the Example Chernivtsi Region).
36.
Šipoš Danijel, Gleich Dušan. (2020). A Lightweight and Low-Power UAV-Borne Ground Penetrating Radar Design for Landmine Detection. Sensors. 20 (8): 2234-2234. doi:10.3390/s20082234.
37.
Israel M., Mende M., Keim S. (2015). UAVRC, A GENERIC MAV FLIGHT ASSISTANCE SOFTWARE. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XL-1/W4: 287-291. doi:10.5194/isprsarchives-xl-1-w4-287-2015.
38.
Jayaweera Herath M. P. C., Hanoun Samer. (2022). Path Planning of Unmanned Aerial Vehicles (UAVs) in Windy Environments. Drones. 6 (5): 101-101. doi:10.3390/drones6050101.
39.
Karpinskyi Yu, Lazorenko-Hevel N. (2018). The methods of geospatial data collection for topographic mapping. Suchasni Dosiahnennia Heodezychnoi Nauky I Vyrobnytstva. I (35): 204-211.
40.
LaValle Steven M. (2006). Planning Algorithms. Cambridge University Press. doi:10.1017/cbo9780511546877.
41.
Levitin AV. (2006). Algoritmy: vvedenie v razrabotku i analiz [Introduction to the design and analysis of algorithms]. : 345-353.
42.
Lopes Bento Nicole, Araújo E Silva Ferraz Gabriel, Alex, re Pena Barata Rafael, Santos Santana Lucas, Diennevan Souza Barbosa Brenon, Conti Leonardo, Becciolini Valentina, Rossi Giuseppe. (2022). Overlap influence in images obtained by an Unmanned Aerial Vehicle on a digital terrain model of altimetric precision. European Journal of Remote Sensing. 55 (1). doi:10.1080/22797254.2022.2054028.
43.
Medvedskyi Yurii, Annenkov Andriy, Adamenko Oleks, r, Demianenko Roman, Tcikolenko Elena. (2024). PECULIARITIES OF UAV MAPPING OF THE TERRITORY WITH A SIGNIFICANT ELEVATION DIFFERENC. Urban development and spatial planning. (85): 391-404. doi:10.32347/2076-815x.2024.85.391-404.
44.
Meier Kilian, Hann Richard, Skaloud Jan, Garreau Arthur. (2022). Wind Estimation with Multirotor UAVs. Atmosphere. 13 (4): 551-551. doi:10.3390/atmos13040551.
45.
Miller Jodi A., Minear Paul D., Niessner Albert F., DeLullo Anthony M., Geiger Brian R., Long Lyle N., Horn Joseph F. (2007). Intelligent Unmanned Air Vehicle Flight Systems. Journal of Aerospace Computing, Information, and Communication. 4 (5): 816-835. doi:10.2514/1.26553.
46.
Moore Edward F. (1959). The shortest path through a maze. Proc. of the International Symposium on the Theory of Switching. 285-292. Harvard University Press.
47.
Nikolos I.K., Valavanis K.P., Tsourveloudis N.C., Kostaras A.N. (2003). Evolutionary algorithm based offline/online path planner for UAV navigation. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics). 33 (6): 898-912. doi:10.1109/tsmcb.2002.804370.
48.
Paredes José, Álvarez Fern, o, Aguilera Teodoro, Villadangos José. (2017). 3D Indoor Positioning of UAVs with Spread Spectrum Ultrasound and Time-of-Flight Cameras. Sensors. 18 (1): 89-89. doi:10.3390/s18010089.
49.
Ramírez-Atencia Cristian, Bello-Orgaz Gema, R-Moreno Maria D., Camacho David. (2014). Branching to Find Feasible Solutions in Unmanned Air Vehicle Mission Planning. In: Intelligent Data Engineering and Automated Learning – IDEAL 2014. 286-294. Springer International Publishing. doi:10.1007/978-3-319-10840-7ES_DASH35.
50.
Ramirez-Atencia Cristian, Camacho David. (2018). Extending QGroundControl for Automated Mission Planning of UAVs. Sensors. 18 (7): 2339-2339. doi:10.3390/s18072339.
51.
Rodríguez-Fernández Víctor, Menéndez Héctor D., Camacho David. (2017). A study on performance metrics and clustering methods for analyzing behavior in UAV operations. Journal of Intelligent and Fuzzy Systems. 32 (2): 1307-1319. doi:10.3233/jifs-169129.
52.
Rusli N, Majid M R, Din A H M. (2014). Google Earth’s derived digital elevation model: A comparative assessment with Aster and SRTM data. IOP Conference Series: Earth and Environmental Science. 18: 012065-012065. doi:10.1088/1755-1315/18/1/012065.
53.
Stecz Wojciech, Gromada Krzysztof. (2020). UAV Mission Planning with SAR Application. Sensors. 20 (4): 1080-1080. doi:10.3390/s20041080.
54.
Stolaroff Joshuah K., Samaras Constantine, O’Neill Emma R., Lubers Alia, Mitchell Alex, ra S., Ceperley Daniel. (2018). Energy use and life cycle greenhouse gas emissions of drones for commercial package delivery. Nature Communications. 9 (1). doi:10.1038/s41467-017-02411-5.
55.
Thibbotuwawa Amila. (2019). Unmanned aerial vehicle fleet mission planning subject to changing weather conditions.
56.
Thibbotuwawa Amila, Bocewicz Grzegorz, Nielsen Peter, Zbigniew Banaszak. (2019). Planning deliveries with UAV routing under weather forecast and energy consumption constraints. IFAC-PapersOnLine. 52 (13): 820-825. doi:10.1016/j.ifacol.2019.11.231.
57.
Thibbotuwawa Amila, Bocewicz Grzegorz, Radzki Grzegorz, Nielsen Peter, Banaszak Zbigniew. (2020). UAV Mission Planning Resistant to Weather Uncertainty. Sensors. 20 (2): 515-515. doi:10.3390/s20020515.
58.
Thibbotuwawa Amila, Nielsen Peter, Zbigniew Banaszak, Bocewicz Grzegorz. (2018). Energy Consumption in Unmanned Aerial Vehicles: A Review of Energy Consumption Models and Their Relation to the UAV Routing. In: Information Systems Architecture and Technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology – ISAT 2018. 173-184. Springer International Publishing. doi:10.1007/978-3-319-99996-8ES_DASH16.
59.
Liang Yang, Juntong Qi, Xiao Jizhong, Xia Yong. (2014). A literature review of UAV 3D path planning. Proceeding of the 11th World Congress on Intelligent Control and Automation. 2376-2381.
60.
Yu Zheng, Sun Fuchun, Lu Xiao, Song Yixu. (2021). Overview of Research on 3D Path Planning Methods for Rotor UAV. 2021 International Conference on Electronics, Circuits and Information Engineering (ECIE). 368-371. doi:10.1109/ecie52353.2021.00081.
61.
Zatserkovnyi I, Tishaiev V, Virshylo I. (2016). Geographic Information Systems in Earth Sciences. Publishing house of Nizhyn State University named after Mykola Gogol.
62.
Chao Zhang, Zhen Ziyang, Daobo Wang, Meng Li. (2010). UAV path planning method based on ant colony optimization. 2010 Chinese Control and Decision Conference. doi:10.1109/ccdc.2010.5498477.
63.
Zhang Miao, Jiang Zhiwen, Wang Lihui, Yao Yiping. (2017). Research on Parallel Ant Colony Algorithm for 3D Terrain Path Planning. In: Modeling, Design and Simulation of Systems. 74-82. Springer Singapore. doi:10.1007/978-981-10-6463-0ES_DASH7.
64.
Zhang Zhengxin, Zhu Lixue. (2023). A Review on Unmanned Aerial Vehicle Remote Sensing: Platforms, Sensors, Data Processing Methods, and Applications. Drones. 7 (6): 398-398. doi:10.3390/drones7060398.
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