Detecting and Avoiding Dangerous Area for UAVs Using Public Big Data


KIPS Transactions on Software and Data Engineering, Vol. 8, No. 6, pp. 243-250, Jun. 2019
https://doi.org/10.3745/KTSDE.2019.8.6.243, Full Text:
Keywords: UAV, Big data, Autopilot, Changes in Flight Environment, Dangerous Area
Abstract

Because of a moving UAV has a lot of potential/kinetic energy, if the UAV falls to the ground, it may have a lot of impact. Because this can lead to human casualities, in this paper, the population density area on the UAV flight path is defined as a dangerous area. The conventional UAV path flight was a passive form in which a UAV moved in accordance with a path preset by a user before the flight. Some UAVs include safety features such as a obstacle avoidance system during flight. Still, it is difficult to respond to changes in the real-time flight environment. Using public Big Data for UAV path flight can improve response to real-time flight environment changes by enabling detection of dangerous areas and avoidance of the areas. Therefore, in this paper, we propose a method to detect and avoid dangerous areas for UAVs by utilizing the Big Data collected in real-time. If the routh is designated according to the destination by the proposed method, the dangerous area is determined in real-time and the flight is made to the optimal bypass path. In further research, we will study ways to increase the quality satisfaction of the images acquired by flying under the avoidance flight plan.


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Cite this article
[IEEE Style]
P. K. Seok, K. M. Jun and K. S. Ho, "Detecting and Avoiding Dangerous Area for UAVs Using Public Big Data," KIPS Transactions on Software and Data Engineering, vol. 8, no. 6, pp. 243-250, 2019. DOI: https://doi.org/10.3745/KTSDE.2019.8.6.243.

[ACM Style]
Park Kyung Seok, Kim Min Jun, and Kim Sung Ho. 2019. Detecting and Avoiding Dangerous Area for UAVs Using Public Big Data. KIPS Transactions on Software and Data Engineering, 8, 6, (2019), 243-250. DOI: https://doi.org/10.3745/KTSDE.2019.8.6.243.