Journal of the Korean Society for Aeronautical & Space Sciences (한국항공우주학회지)

The Korean Society for Aeronautical and Space Sciences (한국항공우주학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on the Improvement of Searching Performance of Autonomous Flight UAVs Based on Flocking Theory

플로킹 이론 기반 자율정찰비행 무인항공기의 탐색성능 향상에 관한 연구

  • Received : 2020.01.18
  • Accepted : 2020.04.20
  • Published : 2020.06.01
Download PDF
⟨ Previous Next ⟩

Abstract

In conducting a mission to explore and track targets using a number of unmanned aerial vehicles(UAVs), performance for that mission may vary significantly depending on the operating conditions of the UAVs such as the number of operations, the altitude, and what future flight paths each aircraft decides based on its current position. However, studies on the number of operations, operating conditions, and flight patterns of unmanned aircraft in these surveillance missions are insufficient. In this study, several types of flight simulations were conducted to detect and determine targets while multiple UAVs were involved in the avoidance of collisions according to various autonomous flight algorithms based by flocking theory, and the results were presented to suggest a more efficient/effective way to control a number of UAVs in target detection missions.

다수의 무인항공기를 이용하여 표적을 탐색 및 추적하는 임무를 수행하는데 있어서 무인항공기의 운용 대수, 비행고도 등 운용 조건뿐만 아니라, 각 비행체들이 어떤 알고리즘을 이용해 비행경로를 결정하느냐에 따라 그 임무에 대한 성과는 크게 달라질 수 있다. 다만 이러한 표적 탐색 임무에서 자율 비행 무인항공기의 운용 방법이 어떠할 때 가장 효과적이며 효율적인지에 대한 연구는 미흡한상태이다. 본 연구에서는 플로킹 이론을 기반을 둔 다양한 자율비행 알고리즘을 활용하여, 다수의 무인 항공기가 서로 충돌을 회피하면서 표적을 탐지하는 임무를 기반으로 비행 시뮬레이션을 수행하고 그 결과를 분석하여, 표적 탐지 임무에서의 다수의 무인항공기를 제어할 수 있는 보다 효율적/효과적인 방안을 제시하였다.

Keywords

References

  1. Shakhatreh, H., Sawalisch, A. H., Ap-Falasha, A., Dou, Z., Almadia, E., Khalif, I. and Guarani, M., "Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges," IEEE Access, 7, 2019, pp. 48572-48634. https://doi.org/10.1109/ACCESS.2019.2909530
  2. Shackler, R., Ap-Garda, M. A., Badrawi, A., Mohamed, A., Khanate, T., Ap-All, A. and Guarani, M., "Design challenges of multi-UAV systems in cyber-physical applications: A comprehensive survey, and future directions," IEEE Communications Surveys and Tutorials, Vol. 21, No. 4, 2019, pp. 3340-3385. https://doi.org/10.1109/COMST.2019.2924143
  3. Gen, L., Hang, Y. F., Wang, J. J., Foh, J. Y. and Ted, S. H., "Mission planning of autonomous UAVs for urban surveillance with evolutionary algorithms," IEEE International Conference on Control and Automation (INCA), IEEE, June 2013, pp. 828-833.
  4. Career, D. W., Beard, R. W., Mcluhan, T. W., Eli, S. M. and Menhir, R. K., "Forest fire monitoring with multiple small UAVs," American Control Conference, IEEE, June 2005, pp. 3530-3535.
  5. Xi, X., Yang, L., Men, W., Cai, Q. and Fun, M., "Multi-Agent Coverage Search in Unknown Environments with Obstacles: A Survey," Chinese Control Conference(CRC), IEEE, July 2019, pp. 2317-2322.
  6. Yang, Y., Miami, A. A. and Polycarpous, M. M., "Decentralized cooperative search by networked UAVs in an uncertain environment," American Control Conference, IEEE, Vol. 6, June 2004, pp. 5558-5563.
  7. Pirie, R. R., Eli, X. R. and Dealable, R., "UAV route planning for joint search and track missions- An information-value approach," IEEE Transactions on Aerospace and Electronic Systems, Vol. 48, No. 3, 2012. pp. 2551-2565. https://doi.org/10.1109/TAES.2012.6237608
  8. Roxburghe, V., Tarbouchi, M. and Lambente, G., "Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning," IEEE Transactions on Industrial Informatics, Vol. 9, No. 1, 2012, pp. 132-141. https://doi.org/10.1109/TII.2012.2198665
  9. Kaiser, J. N., "Effects of Dynamically Weighting Autonomous Rules in an Unmanned Aircraft System(UAV) Flocking Model," Master's Thesis, Air Force Institute of Technology, Wright Patterson ABB, OH, September 2014.
  10. Jones, P. J., "Cooperative area surveillance strategies using multiple unmanned systems," Doctoral dissertation, Georgia Institute of Technology, 2009.
  11. Sock, M. J., "Development of Autonomous Reconnaissance Flight Simulation for Unmanned Aircraft to Derive Flight Operating Condition," Journal of The Korean Society for Aeronautical and Space Sciences, Vol. 47, No. 4, 2019, pp. 266-273. https://doi.org/10.5139/JKSAS.2019.47.4.266
  12. Reynolds, W. C., "Flocks, herds and school: A distributed behavioral model," ACM SONOGRAPH Computer Graphics, Vol. 21, No. 4, July 1987, pp. 25-34. https://doi.org/10.1145/37402.37406
  13. Join, G. and Wang, L., "Multi-Agent Flocking with Angle-based Formation Shape Control," IEEE Transactions on Automatic Control, Vol. 65, Issue 2, February 2020, pp. 817-823. https://doi.org/10.1109/TAC.2019.2917143
  14. Dak, F., Chen, M., Wei, X. and Wang, H., "Swarm Intelligence-Inspired Autonomous Flocking Control in UAV Networks," IEEE Access, Vol. 7, 2019, pp. 61786-61796. https://doi.org/10.1109/ACCESS.2019.2916004
  15. Zhao, W., Chu, H., Hang, M. and Sun, T., "Flocking Control of Fixed-wing UAVs with Cooperative Obstacle Avoidance Capability," IEEE Access, Vol. 7, 2019, pp. 17798-17808. https://doi.org/10.1109/ACCESS.2019.2895643
  16. Vasarhelyi, G., Virago, C., Somorjai, G., Nepusz, T., Eiben, A. E. and Vicsek, T., "Optimized Flopping of Autonomous drones in Confined Environments," Science Robotics, Vol. 3, Issue 20, eaat3536, 2018.
  17. Ludo, X., Eli, X., Eli, S., Jang, Z. and Gaud, X., "Flocking for Multi-agent System with Optimally Rigid Topology Based on Information Weighted Karman Consensus Filter," International Journal of Control, Automation and Systems, Vol. 15, No. 1, 2017, pp. 138-148. https://doi.org/10.1007/s12555-015-0134-8
  18. Narcomania, A., Baker, R., Descale, R., Matchers, B., Kolokotronis, S. O., Kreiswirth, B. and Planet, P. J., "Clusterflock: a flocking algorithm for isolating congruent phylogenetic dataquest," Technical Note, Geoscience, December 2016.
  19. Aramco, J. F., Suit, P. B. and Sousa, J. B., "Multiple UAV area decomposition and coverage," IEEE symposium on computational intelligence for security and defense applications, CISDA, IEEE, April 2013, pp. 30-37.