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

  • 제49권12호
  • /
  • Pages.989-996
  • /
  • 2021
  • /
  • 1225-1348(pISSN)
  • /
  • 2287-6871(eISSN)
한국항공우주학회 (The Korean Society for Aeronautical and Space Sciences)
권호 표지
DOI QR코드

DOI QR Code

배터리 사용량 예측 모델 기반 3차원 UAV 경로 최적화

3-Dimensional UAV Path Optimization Based on Battery Usage Prediction Model

  • 투고 : 2021.10.21
  • 심사 : 2021.11.25
  • 발행 : 2021.12.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

배터리를 동력원으로 사용하는 무인항공기의 경우 배터리 용량이 한정적이기 때문에 임무 수행에 제약이 발생할 수 있다. 이를 최소화하기 위해 임무 지역으로 이동하는 동안 소모되는 배터리를 최소화 하는 것이 중요하다. 또한 임무 계획 단계에서 배터리 소모량 예측 모델을 이용하여 임무 수행 가능성을 사전에 판단할 수 있으며 복귀 시점 선정에 기준이 될 수 있다. 본 논문에서는 3차원 공간에서 환경 요소를 반영한 배터리 사용량 예측 모델을 제안한다. 무인항공기의 비행 기하 관계에 따라 요구 동력을 산출하고 이를 통해 배터리 사용량을 예측하였으며 기존에 제안된 배터리 사용량 예측 기법과 비교를 통해 검증한다. 또한 이를 목적함수로 하여 배터리 사용량을 최소화 하는 비행경로를 생성하고 최단 거리를 목적함수로 하였을 때의 결과와 비교하였다.

In the case of an unmanned aerial vehicle using a battery as a power source, there are restrictions in performing the mission because the battery capacity is limited. To extend the mission capability, it is important to minimize battery usage while the flight to the mission area. In addition, by using the battery usage prediction model, the possibility of mission completeness can be determined and it can be a criterion for selecting an emergent landing point in the mission planning stage. In this paper, we propose a battery usage prediction model considering as one of the environmental factors in the three-dimensional space. The required power is calculated according to the flight geometry of an unmanned aerial vehicle. True battery usage which is predicted from the required power is verified through the comparison with the battery usage prediction model. The optimal flight trajectory that minimizes battery usage is produced and compared with the shortest travel distance.

키워드

과제정보

본 연구는 과학기술정보통신부/산업통상자원부/국토교통부/국토교통과학기술진흥원의 지원으로 수행되었음(과제번호 21DPIW-C153691-03).

참고문헌

  1. Dickson, N. T. H., Hannan, M. A., Hossain Lipu, M. S. and Ker, P. J., "State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review," IEEE Access, Vol. 7, 2019, pp. 136116~136136. https://doi.org/10.1109/access.2019.2942213
  2. Juam, Z., James, F. C., Donald, C. S. and Andrea, C. H., "Energy Consumption models for delivery drones: A comparison and assessment," Transportation Research Part D: Transport and Environment, Vol. 90, 2021.
  3. D'Andrea, R., "Guest Editorial Can Drones Deliver?," IEEE Transactions on Automation Science and Engineering, Vol. 11, No. 3, July 2014, pp. 647~648. https://doi.org/10.1109/TASE.2014.2326952
  4. Asma, T., Sid-Ali, A., Sofiene, D. and Abderrahman, E. M., "Sizing of the Drone Delivery Fleet Considerinf Energy Autonomy," Sustainability, Vol. 10, No. 9, 2018, p. 3344. https://doi.org/10.3390/su10093344
  5. Kevin, D., Jordan, H., Geoffrey, G. M. and Sebastian, M., "Vehicle Routing Problems for Drone Delivery," IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 47, No. 1, January 2017, pp. 70~85. https://doi.org/10.1109/TSMC.2016.2582745
  6. Jeong, H. Y., Song, B. D. and Lee, S., "Truck-drone hybrid delivery routing: Payload-energy dependency and No-Fly zones," International Journal of Production Economics, Vol. 214, 2019, pp. 220~233. https://doi.org/10.1016/j.ijpe.2019.01.010
  7. Joshuah, K. S., Constantine, S., Emma, R. O., Alia, L., Alexandra, S. M. and Daniel, C., "Energy use and life cycle greenhouse gas emissions of drones for commercial package delivery," Nature communications, Vol. 9, No. 1, 2018, pp. 1~13. https://doi.org/10.1038/s41467-017-02088-w
  8. Kirschstein, T., "Comparison of energy demands of drone-based and ground-based parcel delivery services," Transportation Research Part D: Transport and Environment, Vol. 78, 2020, p. 102209. https://doi.org/10.1016/j.trd.2019.102209
  9. Talaeizadeh, A., Antunes, D., Pishkenari, H. and Alasty, A., "Optimal-time quadcopter descent trajectories acoiding the vortex ring and autorotation states," Mechatronics, Vol. 68, 2020, p. 102362. https://doi.org/10.1016/j.mechatronics.2020.102362
  10. U.S. Dept. of Transportation, and FAA, Helicopter Emergencies and Hazards, 2012, pp. 11-8~11-20.
  11. Hwang, M., Cha, H. and Jung, S. Y., "Practical Endurance Estimation for Minimizing Energy Consumption of Multirotor Unmanned Aerial Vehicles," Energies, Vol. 11, No. 9, 2018, p. 2221. https://doi.org/10.3390/en11092221
  12. Kennedy, J. and Eberhart, R., "Particle Swarm Optimization," Proceedings of IEEE International Conference on Neural Networks, Vol. 4, 1995, pp. 1942~1948.
  13. Shi, Y. and Eberhart, R., "A modified particle swarm optimizer," Proceedings of IEEE International Conference on Evolutionary Computation, 1998, pp. 69~73.