Three-Dimensional Path Planning of UAVs in Complex Urban Terrains: A Case Study of Emergency Medicine Delivery in Shanghai (China)

Öz

Unmanned aerial vehicles (UAVs), widely known as drones, are used in various domains for tasks including geological prospecting, e-commerce business, and emergencies. Because of the necessity for fast and efficient delivery for emergency medicine distribution, drones can play crucially important roles by their ability to pass through complex urban environments. Drones might therefore aid people living under strict lockdown conditions during surges cases of COVID-19 or other communicable diseases. Nevertheless, distribution routes are usually planned in two-dimensional space. Moreover, restricted areas in urban aerial domains might be overlooked because of complex environmental considerations. To boost the feasibility of drone use, three-dimensional (3D) path routing can be applied when planning aerial distribution routes for drones, such as those used for delivering emergency medicines. This study specifically examines a more reliable method of using heuristic algorithms and software ArcGIS. After collecting location data of chronic patients in lockdown areas from the Shanghai official information system database, 3D visualization of the terrain and complex airspace was done using ArcGIS. Secondly, UAV routing constraints are summarized according to current laws and regulations for UAV operation at low altitudes. Furthermore, feasible solutions are incorporated into this model. Finally, after improved ant colony optimization (ACO) application to 3D route planning problems, programming was done using MATLAB (ver. 2017b). Assuming guaranteed safety and compliance with regulations, the solutions demonstrate the algorithmic efficiency and provide a satisfactory route plan for emergency medicine delivery that might guide emergency delivery system routing design in similarly complex urban environments.

Anahtar Kelimeler

• 3D route planning
• emergency
• geographic information system
• heuristic algorithm
• medical emergency;
• unmanned aerial vehicle

Kaynakça

1. Choi D S, Hong K J, Shin S D et al. (2021) Effect of topography and weather on delivery of automatic electrical defibrillator by drone for out-of-hospital cardiac arrest. Scientific Reports, 11(1): 1-8.
2. Dorigo M, & Blum C. (2005) Ant colony optimization theory: A survey. Theoretical Computer Science, 344(2-3): 243-278.
3. Hirsch M J, Ortiz-Pena H, & Sudit M. (2011) Decentralized cooperative urban tracking of multiple ground targets by a team of autonomous UAVs. 14th International Conference on Information Fusion. IEEE, 1-7.
4. Khan S I, Qadir Z, Munawar H S et al. (2021) UAVs path planning architecture for effective medical emergency response in future networks. Physical Communication, 47: 101337.
5. Ling, G., & Draghic, N. (2019). Aerial drones for blood delivery. Transfusion, 59(S2), 1608-1611.
6. Marques, M. M., Gatta, M., Barreto, M., Lobo, V., Matos, A., Ferreira, B., ... & Marques, F. (2018, May). Assessment of a Shallow Water Area in the Tagus Estuary Using Unmanned Underwater Vehicle (or AUVs), Vector-Sensors, Unmanned Surface Vehicles, and Hexacopters – REX'17. In 2018 OCEANS-MTS/IEEE Kobe Techno-Oceans (OTO) IEEE. (pp. 1-5).
7. Rosner, D., Trifu, C., Tranca, C., Vasilescu, I., & Stancu, F. (2018, September). Magnetic field sensor for UAV power line acquisition and tracking. In 2018 17th RoEduNet Conference: Networking in Education and Research (RoEduNet). IEEE, 1-5.
8. Saeed F, Mehmood A, Majeed M F et al. (2021) Smart delivery and retrieval of swab collection kit for COVID-19 test using autonomous Unmanned Aerial Vehicles. Physical Communication, 48: 101373.

9. Shanghai Municipal Health Commission. Daily Reports .https://wsjkw.sh.gov.cn/yqtb/index.html.
10. Wang M, Voos H, & Su D. (2018) Robust online obstacle detection and tracking for collision-free navigation of multirotor uavs in complex environments. 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 1228-1234.