Risk Assessment for Aerial Package Delivery
Recent developments in unmanned aerial vehicles (UAVs) have begun to attract intense interest. UAVs started to use for many different applications from military to civilian use. Some online retailer and logistics companies are testing the UAV delivery. UAVs have great potentials to reduce cost and time of deliveries and responding to emergencies in a short time. Despite these great positive sides, just a few works have been done for routing of UAVs for package deliveries. As known, transportation of goods from one place to another may have many hazards on delivery route due to falling hazards that can be exemplified as ground objects or air obstacles. This situation refers to wide-range insurance concept. For this reason, deliveries that are made with drones get into the scope of shipping insurance. On the other hand, air traffic was taken into account in the absence of unmanned aerial vehicle. But now, it has been a reality for aerial fields. In this study, the main goal is to conduct risk analysis of package delivery services using drone, based on delivery routes.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1315499Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1024
 Dorling, K., Heinrichs, J., Messier, G. G., & Magierowski, S. (2017). Vehicle routing problems for drone delivery. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(1), 70-85.
 C. C. Murray and A. G. Chu, “The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery,” Transp. Res. Emerging Technol., vol. 54, pp. 86–109, May 2015.
 K. Sundar and S. Rathinam, “Algorithms for routing an unmanned aerial vehicle in the presence of refueling depots,” IEEE Trans. Autom. Sci.Eng., vol. 11, no. 1, pp. 287–294, Jan. 2014.
 Wang, Xingyin, Stefan Poikonen, and Bruce Golden. "The vehicle routing problem with drones: Several worst-case results." Optimization Letters 11.4 (2017): 679-697.
 Wackwitz, Kay, and Hendrick Boedecker. "Safety Risk Assessment for UAV Operation." Drone Industry Insights, Safe Airspace Integration Project, Part One, Hamburg, Germany (2015).
 Ferrandez, Sergio Mourelo, et al. "Optimization of a truck-drone in tandem delivery network using k-means and genetic algorithm." Journal of Industrial Engineering and Management 9.2 (2016): 374.
 Chen, Chen, Y., Zhao, X., & Han, J. (2010). Review of 3D path planning methods for mobile robot. Robot, 32, 568–576.
 Berry, A., Howitt, J., Gu, D.-W., & Postlethwaite, I. (2010). Enabling the operation of multiple micro-air-vehicles in increasingly complex obstacle-rich environments. In J. Rankin (Ed.), AIAA [email protected] Aerospace 2010, Atlanta, Georgia (p. 1–14). Reston, VA: American Institute of Aeronautics and Astronautics Inc..
 Srikanthakumar, S., Liu, C., & Chen, W.H. (2012). Optimizationbased safety analysis of obstacle avoidance systems for unmanned aerial vehicles. Journal of Intelligent & Robotic Systems, 65(1–4), 219–231.
 Oland, Espen, & Kristiansen, Raymond. (2013). Collision and terrain avoidance for UAVs using the potential field method. In IEEE Aerospace Conference (pp. 1–7). Washington, DC: IEEE Computer Society.
 Koren, Y., & Borenstein, J. (1991). Potential field methods and their inherent limitations for mobile robot navigation. In Proceedings of the IEEE International Conference on Robotics and Automation (pp. 1398–1404). Piscataway, NJ: IEEE.
 Y.-b. Chen, G.-c. Luo, Y.-s. Mei, J.-q. Yu, and X.-l. Su, “UAV path planning using artificial potential field method updated by optimal control theory,” International Journal of Systems Science. Principles and Applications of Systems and Integration, vol. 47, no. 6, pp. 1407–1420, 2016
 Lee, Jaihyun. "Optimization of a modular drone delivery system." Systems Conference (SysCon), 2017 Annual IEEE International. IEEE, 2017.
 K. Dorling, J. Heinrichs, G. G. Messier, and S. Magierowski, “Vehicle Routing Problem for Drone Delivery”, IEEE Transactions on System Man and Cybernetic: Systems, 2016.