Detecting and Avoiding Dangerous Area for UAVs Using Public Big Data

공공 빅데이터를 이용한 UAV 위험구역검출 및 회피방법

  • 박경석 (첨단정보통신융합산업기술원) ;
  • 김민준 (한국정보화진흥원 AI데이터팀) ;
  • 김승호 (경북대학교 컴퓨터학부)
  • Received : 2019.01.30
  • Accepted : 2019.04.10
  • Published : 2019.06.30


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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Fig. 1. Example of Big Data Processing

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Fig. 2. An UAV Operation System

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Fig. 3. An Initial Input Path

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Fig. 4. A Flight Path Algorithm

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Fig. 5. A Workflow for Setting Flight Paths

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Fig. 6. The Proposed Process for Collecting and Processing Public Big Data

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Fig. 7. Flight Dangerous Area Geofencing

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Fig. 8. An Optimal Bypass Flight Path

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Fig. 9. An Example of the Occluded Dangerous Area and the Bypass Flight Pass

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Fig. 10. Flight Path Comparison

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Fig. 11. A Pixhauk Drone

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Fig. 12. A Result using Local Culture Festival Information

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Fig. 13. A Result using the Number of Tag Counts of Transportation Cards

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Fig. 14. A Result using the Integrated Data

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Fig. 15. A Result using the Overlapped Dangerous Area

Table 1. The Number of Tag Counts of Traffic Cards

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Table 2. Query Format for Local Culture Festivals

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Table 3. Response Message Format for Local Culture Festivals

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Table 4. A Quantity Analysis on the same Path Through the Flight Modes

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