UV System Architecture: Challenges and Evolution
Source abstract
Abstract
In recent years, it has become increasingly common to use UVs (unmanned vehicles) in both military and civilian operations. However, the lack of technical information about details such as building, customization, or the architecture used is a problem. In this article, we present the challenges and evolution of UV system architecture. The goal is to offer a general architecture that involves all the components of the system (vehicle, base station, etc.). This work, unlike publications focused on applications, presents the complete architecture of the system: the UV, base station, communications, and, eventually, some computing facilities for application processing. This paper presents the challenges and evaluates an incremental architecture proposal based on a proof of concept that supports evolution. At the end, two options for a UV system architecture are presented.
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Research context
OpenAlex topics: solar cell performance optimization · Aerospace Engineering and Energy Systems · Optical Wireless Communication Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Programa Ingeniería de Sistemas, Facultad de Ingeniería, Universidad Cooperativa de Colombia, Ibagué 730001, Colombia
Facultad de Informática, UNLP, La Plata 1900, Argentina
CIC Provincia de Buenos Aires, La Plata 1900, Argentina
References (44)
Show source-provided references
1. Department of Defense, U.S. Military, and Defense Science Board (2017). The Role of Autonomy in DOD Systems—Reports on Unmanned Aerial Vehicles (UAV), Robotics, Teleoperation, Haptics, Centibot, Remote Presence, Ux V, DARPA Research, and Space and Ground Systems, Department of Defense.
2. Mars Exploration Rovers—NASA (2023, November 23). Nasa.gov, Available online: https://mars.nasa.gov/mer/.
3. Buttazzo, G. (2023). Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications, Springer International Publishing. [4th ed.].10.1007/978-3-031-45410-3 ↗
4. Gian, G., Llerena, J.P., Usero, L., and Patricio, M.A. (2024). A Comparative Study of Convolutional Neural Network and Transformer Architectures for Drone Detection in Thermal Images. Appl. Sci., 15.10.3390/app15010109 ↗
5. Lv, Y., Bo, T., Guo, Q., and Zhang, D. (2024). A Lightweight Small Target Detection Algorithm for UAV Platforms. Appl. Sci., 15.10.3390/app15010012 ↗
6. Communications and Control for Wireless Drone-Based Antenna Array10.1109/TCOMM.2018.2871453 ↗
7. CBDN: Cloud-Based Drone Navigation for Efficient Battery Charging in Drone Networks10.1109/TITS.2018.2883058 ↗
8. Using Reinforcement Learning and Error Models for Drone Precise Landing10.1145/3670997 ↗
9. Katia, K., Mansour, A., Khaldi, M., Clement, B., and Ammad-Uddin, M. (2024). Quadcopters in Smart Agriculture: Applications and Modelling. Appl. Sci., 14.10.3390/app14199132 ↗
10. Kim, H., and Choi, K. (November, January 30). A modular wireless sensor network for architecture of autonomous UAV using dual platform for assisting rescue operation. Proceedings of the 2016 IEEE SENSORS, Orlando, FL, USA.10.1109/ICSENS.2016.7808895 ↗
11. Besada, J.A., Bernardos, A.M., Bergesio, L., Vaquero, D., Campana, I., and Casar, J.R. (2019, January 11–15). Drones-as-a-service: A management architecture to provide mission planning, resource brokerage and operation support for fleets of drones. Proceedings of the 2019 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019, Kyoto, Japan.10.1109/PERCOMW.2019.8730838 ↗
12. Drone networks: Communications, coordination, and sensing10.1016/j.adhoc.2017.09.001 ↗
13. Cabuk, U.C., Tosun, M., Dagdeviren, O., and Ozturk, Y. (December, January 28). An Architectural Design for Autonomous and Networked Drones. Proceedings of the IEEE Military Communications Conference MILCOM, Rockville, MD, USA.
14. Reference architecture specification for drone systems10.1016/j.micpro.2022.104705 ↗
15. Hussein, M., and Nouacer, R. (2020, January 13–17). Towards an Architecture for Customizable Drones. Proceedings of the 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020, Madrid, Spain.10.1109/COMPSAC48688.2020.00019 ↗
16. A survey of prototype and experiment for UAV communications10.1007/s11432-020-3030-2 ↗
17. Bigazzi, L., Basso, M., Boni, E., Innocenti, G., and Pieraccini, M. (2021). A Multilevel Architecture for Autonomous UAVs. Drones, 5.10.3390/drones5030055 ↗
18. System Architecture for Autonomous Drone-Based Remote Sensing
19. Gromada, K.A., and Stecz, W.M. (2022). Designing a reliable uav architecture operating in a real environment. Appl. Sci., 12.10.3390/app12010294 ↗
20. Tinetti, F.G., and Riveros, O.C.V. (2017, January 14–16). Unmanned Vehicles: Infrastructure and Power Consumption Measurements. Proceedings of the 2017 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA.10.1109/CSCI.2017.115 ↗
21. Steup, C., Beckhaus, J., and Mostaghim, S. (2021). A single-copter UWB-ranging-based localization system extendable to a swarm of drones. Drones, 5.10.3390/drones5030085 ↗
22. Real-time drone formation control for group display10.1007/978-3-030-19063-7_63 ↗
23. A multiple-drone arc routing and mothership coordination problem10.1016/j.cor.2023.106322 ↗
24. Tinetti, F.G., and Riveros, O.C.V. (2018, January 13–15). Unmanned Vehicles: Towards Heterogeneous Hardware Approaches. Proceedings of the 2018 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA.10.1109/CSCI46756.2018.00181 ↗
25. SYMA (2025, January 13). SYMA X5HW FPV REAL-TIME THE NEW DRONE—Smart Drone—SYMA Official Site. Available online: https://www.symatoys.com/goodshow/x5hw-syma-x5hw-fpv-real-time-the-new-drone.html.
26. Khavnekar, A., Gondalia, J., and Shah, D. (2017, January 19–20). Low-Cost arduino based architecture for simulation and control of quadrotors. Proceedings of the 2017 International Conference on Inventive Systems and Control (ICISC), Coimbatore, India.10.1109/ICISC.2017.8068738 ↗
27. Nguyen, H.-Q., Loan, T.T.K., Mao, B.D., and Huh, E.-N. (2015, January 7–10). Low cost real-time system monitoring using Raspberry Pi. Proceedings of the 2015 Seventh International Conference on Ubiquitous and Future Networks, Sapporo, Japan.
28. Agrawal, N., and Singhal, S. (2015, January 15–16). Smart drip irrigation system using raspberry pi and arduino. Proceedings of the International Conference on Computing, Communication & Automation, Noida, India.10.1109/CCAA.2015.7148526 ↗
29. Tuan, K.N. (2019, January 20–21). A Wireless Sensor Network for Aquaculture Using Raspberry Pi, Arduino and Xbee. Proceedings of the 2019 International Conference on System Science and Engineering, ICSSE 2019, Dong Hoi, Vietnam.10.1109/ICSSE.2019.8823104 ↗
30. Krauss, R. (2016, January 6–8). Combining Raspberry Pi and Arduino to form a low-cost, real-time autonomous vehicle platform. Proceedings of the 2016 American Control Conference (ACC), Boston, MA, USA.10.1109/ACC.2016.7526714 ↗
31. Mobile Robot Platform with Arduino Uno and Raspberry Pi for Autonomous Navigation10.1016/j.promfg.2019.02.254 ↗
32. Tinetti, F.G., Riveros, O.C.V., and Romero, F.L. (2019, January 5–7). Unmanned Vehicles: Real Time Problems in Drone Receivers. Proceedings of the 2019 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA.10.1109/CSCI49370.2019.00205 ↗
33. FlySky (2025, January 13). FS-IA6B. Available online: https://www.flysky-cn.com/ia6b-canshu.
34. FlySky (2025, January 13). FS-i6. Available online: https://www.flysky-cn.com/fsi6.
35. NodeMcu Team (2025, January 13). NodeMcu—An Open-Source Firmware Based on ESP8266 Wifi-Soc. Available online: https://www.nodemcu.com/index_en.html.
36. Texas Instrument (2025, January 13). CMOS Quad 2-Input AND Gate. Available online: https://www.ti.com/product/CD4081B.
37. Ryze (2025, January 13). Tello. Available online: https://www.ryzerobotics.com/tello.
38. Tinetti, F.G., and Riveros, O.C.V. (2021, January 15–17). Combining Technologies for Aiding Search Missions with Drones. Proceedings of the 2021 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA.10.1109/CSCI54926.2021.00013 ↗
39. Wang, S., Han, Y., Chen, J., Zhang, Z., Wang, G., and Du, N. (2018, January 10–12). A Deep-Learning-Based Sea Search and Rescue Algorithm by UAV Remote Sensing. Proceedings of the 2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC), Xiamen, China.10.1109/GNCC42960.2018.9019134 ↗
40. Lauterbach, H.A., Koch, C.B., Hess, R., Eck, D., Schilling, K., and Nüchter, A. (2019, January 2–4). The Eins3D project—Instantaneous UAV-Based 3D Mapping for Search and Rescue Applications. Proceedings of the 2019 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), Würzburg, Germany.10.1109/SSRR.2019.8848972 ↗
41. UAV-Based Real-Time Survivor Detection System in Post-Disaster Search and Rescue Operations10.1109/JMASS.2021.3083659 ↗
42. Charalampidou, S., Lygouras, E., Dokas, I., Gasteratos, A., and Zacharopoulou, A. (2020, January 1–4). A Sociotechnical Approach to UAV Safety for Search and Rescue Missions. Proceedings of the 2020 International Conference on Unmanned Aircraft Systems (ICUAS), Athens, Greece.10.1109/ICUAS48674.2020.9213921 ↗
43. Papić, V., Šolić, P., Milan, A., Gotovac, S., and Polić, M. (2021). High-resolution image transmission from uav to ground station for search and rescue missions planning. Appl. Sci., 11.10.3390/app11052105 ↗
44. Zhang, S., and Zeng, Q. (2024). Online Unmanned Ground Vehicle Path Planning Based on Multi-Attribute Intelligent Reinforcement Learning for Mine Search and Rescue. Appl. Sci., 14.10.3390/app14199127 ↗