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

The Korean Society for Aeronautical and Space Sciences (한국항공우주학회)
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Multi-UAV Mission Allocation and Optimization Technique Based on Discrete-Event Modeling and Simulation

이산 사건 모델링 및 시뮬레이션 기반의 다수 무인기 임무 할당 및 최적화 기법

  • Lee, Dong Ho (Aerospace Technology Research Institute, Agency for Defense Development) ;
  • Jang, Hwanchol (Aerospace Technology Research Institute, Agency for Defense Development) ;
  • Kim, Sang-Hwan (Aerospace Technology Research Institute, Agency for Defense Development) ;
  • Chang, Woohyuk (Aerospace Technology Research Institute, Agency for Defense Development)
  • Received : 2019.09.18
  • Accepted : 2020.01.15
  • Published : 2020.02.01
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Abstract

In this paper, we propose a heterogenous mission allocation technique for multi-UAV system based on discrete event modeling. We model a series of heterogenous mission creation, mission allocation, UAV departure, mission completion, and UAV maintenance and repair process as a mathematical discrete event model. Based on the proposed model, we then optimize the number of UAVs required to operate in a given scenario. To validate the optimized number of UAVs, the simulations are executed repeatedly, and their results are analyzed. The proposed mission allocation technique can be used to efficiently utilize limited UAV resources, and allow the human operator to establish an optimal mission plan.

본 연구는 복잡한 다수 무인기 시스템을 효율적으로 운용하기 위해 이산 사건 모델 기반의 다수 무인기 이종 임무 할당 기법을 제안한다. 이종 임무의 생성, 임무 할당, 무인기 출동, 임무 수행, 무인기 수리 및 정비에 이르는 무인기의 임무 시작에서 종료까지 일련의 과정을 이산 사건 모델링을 통해 수학적으로 정의하고, 이를 기반으로 주어진 임무 상황에서 필요한 최적 무인기 대수를 도출한다. 도출된 최적 무인기 대수를 적용하여 이산 사건 모델 기반의 반복 시뮬레이션을 통해 그 결과를 검증한다. 제안된 이종 임무 할당 기법으로 운용자는 제한된 무인기 자원을 효율적으로 활용하여 주어진 임무 상황에서 최적의 임무 계획을 수립할 수 있다.

Keywords

References

  1. Rossetti, M. D., Simulation modeling and Arena, 2nd Ed., John Wiley & Sons, Hoboken, NJ, 2016, pp. 1-6.
  2. Kelton, W. D., Sadowski, R. P., and Zupick, N. B., Simulation with Arena, 6th Ed., McGraw-Hill Education, New York, 2015, p. 677.
  3. Arena Simulation, https://www.arenasimulation.com/.
  4. Bellinghan, J., Tillerson, M., Richards, A., and How, J. P., "Multi-Task Allocation and Path Planning for Cooperating UAVs," Cooperative Control: Models, Applications and Algorithms, Vol. 1, 2003, pp. 23-41. https://doi.org/10.1007/978-1-4757-3758-5_2
  5. Leary, S., Deittert, M., and Bookless, J., "Constrained UAV mission planning: A comparison of approaches," 2011 IEEE International Conference on Computer Vision Workshops, 2011, pp. 2002-2009.
  6. Park, H. J., Jeong, S. S., and Ahn, J. M., "Design of a multiple-UAS mission model using activity-based modeling," 14th International Conference on Control, Automation and Systems, 2014, pp. 848-851.
  7. Dietrich T., Krug S., and Zimmermann A., "A discrete event simulation and evaluation framework for multi UAV system maintenance processes," 2017 IEEE International Systems Engineering Symposium, 2017, pp. 1-6.
  8. Lee, D. H., Chang, W., and Byun, J., "Discrete-event Modeling and Simulation of Autonomous Multi-UAV Mission Management," Avionics Systems Symposium Korea, 2018, p. 203.
  9. Lee, D. H., Chang, W., and Byun, J., "An Optimization Technique for Discrete Event Model-based UAV Real-Time Heterogeneous Mission Allocation," Proceeding of the Korean Society for Aeronautical and Space Sciences Fall Conference, November 2018, pp. 278-279.