KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Detecting and Avoiding Dangerous Area for UAVs Using Public Big Data

공공 빅데이터를 이용한 UAV 위험구역검출 및 회피방법

  • Received : 2019.01.30
  • Accepted : 2019.04.10
  • Published : 2019.06.30
Download PDF
⟨ Previous Next ⟩

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.

움직이는 UAV는 많은 위치에너지와 운동에너지를 가지므로 지상으로 추락하는 경우 많은 충격량을 가질 수 있다. 이는 인명피해로 연결될 수 있기 때문에 본 논문에서는 UAV 비행경로 상의 인구밀집지역을 위험구역으로 정의하였다. 기존의 UAV 경로비행은 사용자에 의해 미리 설정된 경로만을 운행하는 수동적인 형태였다. 일부 UAV는 경로비행 중 장애물을 회피하는 시스템 등 안전기능을 포함하고 있지만, 실시간 비행환경변화에 대응하기에는 부족하다. UAV 경로비행에 공공 빅데이터를 활용할 경우, 위험구역을 검출하고 회피비행을 수행할 수 있어서 실시간 비행환경변화에 대한 대응이 향상될 수 있다. 따라서 본 논문에서는 실시간으로 수집된 빅데이터를 활용하여 위험구역을 회피하는 최적경로 비행 방안을 제안한다. 실험 결과, 제안하는 자동경로비행에서 목적지와 목적지에 따른 경로를 지정할 경우, 실시간으로 위험지역을 판단하여 최적 우회경로로 비행하는 것을 확인하였다. 추후 회피방안에 따라 비행하여 획득하는 영상의 질적 만족도를 높일 수 있는 방안을 연구할 예정이다.

Keywords

JBCRJM_2019_v8n6_243_f0001.png 이미지

Fig. 1. Example of Big Data Processing

JBCRJM_2019_v8n6_243_f0002.png 이미지

Fig. 2. An UAV Operation System

JBCRJM_2019_v8n6_243_f0003.png 이미지

Fig. 3. An Initial Input Path

JBCRJM_2019_v8n6_243_f0004.png 이미지

Fig. 4. A Flight Path Algorithm

JBCRJM_2019_v8n6_243_f0005.png 이미지

Fig. 5. A Workflow for Setting Flight Paths

JBCRJM_2019_v8n6_243_f0006.png 이미지

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

JBCRJM_2019_v8n6_243_f0007.png 이미지

Fig. 7. Flight Dangerous Area Geofencing

JBCRJM_2019_v8n6_243_f0008.png 이미지

Fig. 8. An Optimal Bypass Flight Path

JBCRJM_2019_v8n6_243_f0009.png 이미지

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

JBCRJM_2019_v8n6_243_f0010.png 이미지

Fig. 10. Flight Path Comparison

JBCRJM_2019_v8n6_243_f0011.png 이미지

Fig. 11. A Pixhauk Drone

JBCRJM_2019_v8n6_243_f0012.png 이미지

Fig. 12. A Result using Local Culture Festival Information

JBCRJM_2019_v8n6_243_f0013.png 이미지

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

JBCRJM_2019_v8n6_243_f0014.png 이미지

Fig. 14. A Result using the Integrated Data

JBCRJM_2019_v8n6_243_f0015.png 이미지

Fig. 15. A Result using the Overlapped Dangerous Area

Table 1. The Number of Tag Counts of Traffic Cards

JBCRJM_2019_v8n6_243_t0001.png 이미지

Table 2. Query Format for Local Culture Festivals

JBCRJM_2019_v8n6_243_t0002.png 이미지

Table 3. Response Message Format for Local Culture Festivals

JBCRJM_2019_v8n6_243_t0003.png 이미지

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

JBCRJM_2019_v8n6_243_t0004.png 이미지

References

  1. Zhenyu Zhou, Caixia Gao, Chen Xu, Yan Zhang, Shahid Mumtaz, and Jonathan Rodriguez, "Social Big-Data-Based Content Dissemination in Internet of Vehicles," IEEE Transactions on Industrial Engineering, Vol.14, No.2, pp.768-777, 2018. https://doi.org/10.1109/TII.2017.2733001
  2. Kai Lin, Jiming Luo, Long Hu, M. Shamim Hossain, and Ahmed Ghoneim, "Localization Based on Social Big Data Analysis in the Vehicular Networks," IEEE Transactions on Industrial Engineering, Vol.13, No.4, pp.1932-1940, 2017. https://doi.org/10.1109/TII.2016.2641467
  3. S. J. Lee and D. H. Lee, "Real Time Predictive Analytic System Design and Implementation using Bigdata-log," Journal of the Korea Institute of Information Security & Cryptology, Vol.25, No.6, pp.1399-1410, 2015. https://doi.org/10.13089/JKIISC.2015.25.6.1399
  4. J. H. Lee, "Building an SNS Crawling System Using Python," Journal of the Korea Industrial Information Systems Research, Vol.23, No.5, pp.61-76, 2018. https://doi.org/10.9723/JKSIIS.2018.23.5.061
  5. R. Kune, P. K. Konugurthi, A. Agarwal, R. R. Chillarige, and R. Buyya, "The anatomy of Big Data Computing", Softw. Pract. Exper., Vol.46, No.1, pp.79-105, Jan. 2016. https://doi.org/10.1002/spe.2374
  6. G. George and D. Lavie, "Big Data and Data Science Methods for Management Research," Academy of Management Journal, Vol.59, No.5, pp.1493-1507, 2016. https://doi.org/10.5465/amj.2016.4005
  7. D. M. Park, "Automated Time Series Content Analysis with News Big Data Analytics : Analyzing Sources and Quotes in One Million News Articles for 26 Years," Korean Journal of Journalism & Communication Studies, Vol.60, No.5, pp. 353-407, 2016. https://doi.org/10.20879/kjjcs.2016.60.5.013
  8. J. H. Moon, J. W. Park, S. H. Han, and E. J. Hwang, "Power Consumption Forecasting Scheme for Educational Institutions Based on Analysis of Similar Time Series Data," Journal of KIISE, Vol.44, No.9, pp.954-965, 2017. https://doi.org/10.5626/JOK.2017.44.9.954
  9. J. H. Kwak and Y. S. Sung, "Autonomous UAV Flight Control for GPS-Based Navigation," IEEE Transactions on Industrial Engineering, Vol.6, pp.37947-37955, 2018.
  10. Y. Zeng, R. Zhang, and T. J. Lim, "Wireless Communications with Unmanned Aerial Vehicles: Opportunities and Challenges," IEEE Commun. Mag., Vol.54, No.5, pp.36-42, May 2016. https://doi.org/10.1109/MCOM.2016.7470933
  11. S. Zhang, Y. Zhou, Z. Li, and W. Pan, "Grey Wolf Optimizer for Unmanned Combat Aerial Vehicle Path Planning," Adv. Eng. Softw., Vol.99, pp.121-136, Sep. 2016. https://doi.org/10.1016/j.advengsoft.2016.05.015
  12. V. Roberge, M. Tarbouchi, and G. Labonte, "Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-time UAV Path Planning," IEEE Transactions on Industrial Engineering, Ind. Informat., Vol.9, No.1, pp.132-141, Feb. 2013. https://doi.org/10.1109/TII.2012.2198665
  13. M. Lungu, R. Lungu, and C. Rotaru, "New Systems for Identification Estimation and Adaptive Control of the Aircrafts Movement," Stud. Inform. Control, Vol.20, No.3, pp.273-284, 2011.
  14. I. Lim and S. Ra, "Waypoints Altitude Planning for Terrain Collision Avoidance and Manueverability of an Unmanned Aerial Vehicle," The Journal of Korean Institute of Information Technolgy(JKIIT), Vol.16, No.9, pp.31-41, 2018.
  15. N. Gageik, P. Benz, and S. Montenegro, "Obstacle Detection and Collision Avoidance for a UAV with Complementary Low-cost Sensors," IEEE Access, Vol.3, pp.599-609, 2015. https://doi.org/10.1109/ACCESS.2015.2432455
  16. R. He, R. Wei, and Q. Zhang, "UAV Autonomous Collision Avoidance Approach," Automatika, Vol.58, No.2, pp.195-204, 2017. https://doi.org/10.1080/00051144.2017.1388646