Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Probability-Based Target Search Method by Collaboration of Drones with Different Altitudes

고도를 달리하는 드론들의 협력에 의한 확률기반 목표물 탐색 방법

  • Ha, Il-Kyu (Department of Computer Engineering, Kyungil University)
  • Received : 2017.10.06
  • Accepted : 2017.10.24
  • Published : 2017.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

For the drone that is active in a wide search area, the time to grasp the target in the field of applications such as searching for emergency patients, monitoring of natural disasters requiring prompt warning and response, that is, the speediness of target detection is very important. In the actual operation of drone, the time for target detection is highly related to collaboration between drones and search algorithm to efficiently search the navigation area. In this research, we will provide a search method with cooperation of drone based on target existence probability to solve the problem of quickness in drone target search. In particular, the proposed method increases the probability of finding a target and shorten the search time by transmitting high-altitude drone search results to a low-altitude drone after searching first and performing more precise search. We verify the performance of the proposed method through several simulations.

넓은 탐색영역에서 활동하는 드론에서 신속한 처치를 요하는 응급환자의 탐색, 신속한 경보와 대응을 요하는 자연재해의 감시와 같은 응용 분야에서 목표물 파악의 시간(time), 즉 신속성의 문제는 매우 중요한 문제가 된다. 드론의 실제 운영에 있어서 목표물을 파악하는 시간은 탐색 영역을 효율적으로 탐색하기 위한 탐색 알고리즘 및 드론 간의 협업과 매우 연관성이 깊다. 따라서 본 연구에서는 드론을 이용한 목표물 탐색에 있어서 신속성의 문제를 해결하기 위하여, 고도를 달리하는 드론들의 협력에 의한 확률기반 목표물 탐색 방법을 제안한다. 특히 제안한 방법은 고(高)고도 드론이 우선 탐색을 실시하고, 탐색 결과를 저(低)고도 드론에 전달하여 보다 정밀한 탐색을 함으로써 탐색 시간을 줄이고 목표물 발견의 확률을 높이는 방법이다. 시뮬레이션을 통하여 제안된 방법의 성능을 분석한다.

Keywords

References

  1. D. Kim, J. Shin and J. Kim, "Design and Implementation of Wi-Fi based Drone to Save People in Maritime," Journal of the Korea Institute of Information and Communication Engineering, vol.21, no.1, pp.53-60, Jan. 2017. https://doi.org/10.6109/jkiice.2017.21.1.53
  2. S. Kim, "For summarizing the collected information like the airway of unmaned aerial vehicles drone Enhancing Snippet Extraction Method using Fuzzy Inclusion Relation and K-means Clustering," Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology, vol.6, no.6, pp.479-488, June 2016.
  3. I. Bekmezci, O. Sahingoz and S. Temel, "Flying Ad-Hoc Networks (FANETs): A survey," Ad Hoc Networks, vol. 11, pp.1254-1270, May 2013. https://doi.org/10.1016/j.adhoc.2012.12.004
  4. O. Sahingoz, "Networking Models in Flying Ad-Hoc Networks (FANETs): Concepts and Challenges," Journal Intelligence Robot System, vol. 74, pp.513-527, Apr. 2014. https://doi.org/10.1007/s10846-013-9959-7
  5. S. Waharte and N. Trigoni, "Supporting Search and Rescue Operations with UAVs," in Proceedings of the International Conference on Emerging Security Technologies(EST), pp.142-147, 2010.
  6. D. Kim, J. Lee, J. Choi and K. Kim, "A POMDP Framework for Dynamic Task Allocation and Reconnaissance of Multiple Unmanned Aerial Vehicles," Journal of KIISE(The Korean Institute of Information Scientists and Engineers), vol. 39, no. 6, pp.453-463, Feb. 2012.
  7. T. Chung and J. Burdick, "A Decision-Making framework for control strategies in probabilistic search," in Robotics and Automation, 2007 IEEE International Conference on, pp. 4386-4393, 2007.
  8. S. Waharte and N. Trigoni, "Coordinated Search with a Swarm of UAVs," in Proceedings of the 2009 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks Workshops, pp.1-3, 2009.
  9. A. Symington, S. Waharte, S. J. Julier, and N. Trigoni, "Probabilistic target detection by camera-equipped uavs," in Proceedings of the 2010 IEEE International Conference on Robotics and Automation, pp.4076-4082, 2010.
  10. N. Trigoni, S. Waharte, and A. Symington, "Probabilistic search with agile uavs," in Proceedings of the 2010 IEEE International Conference on Robotics and Automation, pp. 2840-2845, 2010.
  11. I. Ha, "An Improvement of the Search Algorithm for Drone's Target Quick Searching," in Proceedings of spring conference of Korea Multimedia Society, vol. 20, no. 1, pp.554-556, 2017.
  12. M. Deisenroth, D. Fox, and C. Edward, "Gaussian Processes for Data-Efficient Learning in Robotics and Control," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 37, no. 2, pp.408-423, Nov. 2015. https://doi.org/10.1109/TPAMI.2013.218
  13. P. Smaragdis, C. Fevotte, G. Mysore, N. Mohammadiha and M. Hoffman, "Static and Dynamic Source Separation Using Nonnegative Factorizations: A unified view," IEEE Signal Processing Magazine, vol. 31, no. 3, pp.66-75, May 2014. https://doi.org/10.1109/MSP.2013.2297715
  14. N. Schneider and D. Gavrila, "Pedestrian Path Prediction with Recursive Bayesian Filters: A Comparative Study," in Proceedings of German Conference on Pattern Recognition 2013, pp.174-183, 2013.
  15. P. Zarco-Tejada, R. Diaz-Varelaa, V. Angileria, and P. Loudjani, "Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods," European Journal of Agronomy, vol. 55, pp.89-99, Apr. 2014. https://doi.org/10.1016/j.eja.2014.01.004