항공우주시스템공학회지 (Journal of Aerospace System Engineering)

  • 제17권2호
  • /
  • Pages.78-85
  • /
  • 2023
  • /
  • 1976-6300(pISSN)
  • /
  • 2508-7150(eISSN)
항공우주시스템공학회 (The Society for Aerospace System Engineering)
권호 표지
DOI QR코드

DOI QR Code

시간 제한 조건을 고려한 유전 알고리즘 기반 다수 무인기 임무계획기법

Genetic algorithm based multi-UAV mission planning method considering temporal constraints

  • 투고 : 2023.01.10
  • 심사 : 2023.03.02
  • 발행 : 2023.04.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

다수 무인기 체계에서 임무할당은 임무 수행 능력을 결정하는 중요한 요인이다. 본 논문은 유전 알고리즘에 기반한 임무계획기법을 제안한다. 본 기법을 통해 제한 조건을 만족하면서, 임무 완료 시간을 최소화하는 해를 구할 수 있다. 임무 할당 문제의 최적해를 구하기 위해서는 계산량이 많이 필요하므로 본 기법이 해를 구하는 대안이 될 수 있다. 본 기법은 현실 세계의 다양한 종류의 무인기, 임무, 제한 조건을 고려하였다. 제안된 기법은 각 무인기의 임무 시퀀스와 제한 조건 만족을 위한 임무 별 대기 시간을 도출한다. 다양한 수치적 시뮬레이션 결과를 통해 임무 종료 시간을 최소화하는 임무계획 기법의 성능을 확인하였다.

For Multi-UAV systems, a task allocation could be a key factor to determine the capability to perform a task. In this paper, we proposed a task allocation method based on genetic algorithm for minimizing makespan and satisfying various constraints. To obtain the optimal solution of the task allocation problem, a huge calculation effort is necessary. Therefore, a genetic algorithm-based method could be an alternative to get the answer. Many types of UAVs, tasks, and constraints in real worlds are introduced and considered when tasks are assigned. The proposed method can build the task sequence of each UAV and calculate waiting time before beginning tasks related to constraints. After initial task allocation with a genetic algorithm, waiting time is added to satisfy constraints. Multiple numerical simulation results validated the performance of this mission planning method with minimized makespan.

키워드

과제정보

이 논문은 2022년도 정부(방위사업청)의 재원으로 국방기술진흥연구소의 지원을 받아 수행된 연구임 (No. KRIT-CT-21-009), 전장정보 기반 실시간 자동임무실행/수정기술 개발)

참고문헌

  1. Katoch, Sourabh, Sumit Singh Chauhan, and Vijay Kumar. "A review on genetic algorithm: past, present, and future." Multimedia Tools and Applications 80.5 (2021): 8091-8126. https://doi.org/10.1007/s11042-020-10139-6
  2. Kramer, Oliver. "Genetic algorithms." Genetic algorithm essentials. Springer, Cham, 2017. 11-19.
  3. Onn Shehory, Sarit Kraus, "Methods for task allocation via agent coalition formation," Artificial Intelligence, Volume 101, Issues 1-2, pp. 165-200, 1998. https://doi.org/10.1016/S0004-3702(98)00045-9
  4. Scherer, Jurgen, and Bernhard Rinner. "Multi-UAV surveillance with minimum information idleness and latency constraints." IEEE Robotics and Automation Letters 5.3 (2020): 4812-4819. https://doi.org/10.1109/LRA.2020.3003884
  5. Das, Dyutimoy Nirupam, et al. "Synchronized truck and drone routing in package delivery logistics." IEEE Transactions on Intelligent Transportation Systems 22.9 (2020): 5772-5782. https://doi.org/10.1109/TITS.2020.2992549
  6. Waibel, Markus, Bill Keays, and Federico Augugliaro. Drone shows: Creative potential and best practices. ETH Zurich, 2017.
  7. Schwalb, Eddie, and Lluis Vila. "Temporal constraints: A survey." Constraints 3.2 (1998): 129-149. https://doi.org/10.1023/A:1009717525330
  8. Schumacher, Corey, et al. "UAV task assignment with timing constraints via mixed-integer linear programming." AIAA 3rd" Unmanned Unlimited" Technical Conference, Workshop and Exhibit . 2004.
  9. M. Alighanbari, Y. Kuwata and J. P. How, "Coordination and control of multiple UAVs with timing constraints and loitering," Proceedings of the 2003 American Control Conference, 2003.
  10. Attiya, Gamal, and Yskandar Hamam. "Task allocation for maximizing reliability of distributed systems: A simulated annealing approach." Journal of parallel and Distributed Computing 66.10 (2006): 1259-1266. https://doi.org/10.1016/j.jpdc.2006.06.006
  11. Eun, Yeonju, and Hyochoong Bang. "Cooperative task assignment/path planning of multiple unmanned aerial vehicles using genetic algorithm." Journal of aircraft 46.1 (2009): 338-343. https://doi.org/10.2514/1.38510
  12. Lipowski, Adam, and Dorota Lipowska. "Roulette-wheel selection via stochastic acceptance." Physica A: Statistical Mechanics and its Applications 391.6 (2012): 2193-2196. https://doi.org/10.1016/j.physa.2011.12.004
  13. Xu, Guangtong, et al. "Target-bundled genetic algorithm for multi-unmanned aerial vehicle cooperative task assignment considering precedence constraints." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 234.3(2020): 760-773. https://doi.org/10.1177/0954410019883106
  14. Zhao, Yiyang, et al. "Cooperative multiple task assignment problem with target precedence constraints using a waitable path coordination and modified genetic algorithm." IEEE Access 9 (2021): 39392-39410. https://doi.org/10.1109/ACCESS.2021.3063263
  15. Omara, Fatma A., and Mona M. Arafa. "Genetic algorithms for task scheduling problem." Foundations of Computational Intelligence Volume 3. Springer, Berlin, Heidelberg, 2009. 479-507.
  16. Kim, J. H. "Mid to Long Term R&D Direction of UAV for Disaster." Journal of Aerospace System Engineering, 14(5), (2020): 83-90. https://doi.org/10.20910/JASE.2020.14.5.83
  17. Kim, M. J., & Lee, M. K. "Conceptual Design and Study on the Performance Enhancement of Tilt Rotor UAV for Disaster and Policing Operation." Journal of Aerospace System Engineering, 15(1), (2021): 40-46. https://doi.org/10.20910/JASE.2021.15.1.40