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

The Korean Society for Aeronautical and Space Sciences (한국항공우주학회)
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Multiple Drones Collision Avoidance in Path Segment Using Speed Profile Optimization

다수 드론의 충돌 회피를 위한 경로점 구간 속도 프로파일 최적화

  • Received : 2021.12.23
  • Accepted : 2022.10.24
  • Published : 2022.11.01
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Abstract

In an environment where multiple drones are operated, collisions can occur when path points overlap, and collision avoidance in preparation for this is essential. When multiple drones perform multiple tasks, it is not appropriate to use a method to generate a collision-avoiding path in the path planning phase because the path of the drone is complex and there are too many collision prediction points. In this paper, we generate a path through a commonly used path generation algorithm and propose a collision avoidance method using speed profile optimization from that path segment. The safe distance between drones was considered at the expected point of collision between paths of drones, and it was designed to assign a speed profile to the path segment. The optimization problem was defined by setting the distance between drones as variables in the flight time equation. We constructed the constraints through linearize and convexification, and compared the computation time of SQP and convex optimization method in multiple drone operating environments. Finally, we confirmed whether the results of performing convex optimization in the 20 drone operating environments were suitable for the multiple drone operating system proposed in this study.

다수의 드론이 운용되는 환경에서 경로점이 겹칠 때 충돌이 발생할 수 있으며 이를 대비한 충돌 회피는 필수적이다. 다수의 드론이 여러 임무를 수행하는 경우 드론의 경로가 복잡하고 충돌 예상점이 너무 많아 경로계획 단계에서 충돌을 회피하는 경로를 생성하는 방법을 사용하는 것은 적합하지 않다. 본 논문에서는 일반적으로 사용하는 경로 생성 알고리즘을 통해 경로를 생성하고, 그 경로에서 속도 프로파일 최적화를 이용한 충돌 회피 방법을 제안한다. 드론의 경로 간 충돌 예상점에서 드론 사이의 안전거리를 고려하였고, 경로 구간에 속도 프로파일을 할당하도록 설계하였다. 최적화 문제는 드론 간 거리에 대한 식을 비행시간을 변수로 두어 정의하였다. 선형화와 컨벡스화를 통해 구속 조건을 구성하고, 다수 드론 운용 환경에서 SQP(Sequential Quadratic Programming)알고리즘과 컨벡스 최적화 기법의 연산시간을 비교하였다. 마지막으로 20대 드론 운용 환경에서 컨벡스 최적화를 수행한 결과가 본 연구에서 제시한 다수 드론 운용에 적합한지 확인하였다.

Keywords

Acknowledgement

본 연구는 과학기술정보통신부/산업통상자원부/국토교통부/국토교통과학기술진흥원의 지원으로 수행되었음(과제번호 22DPIW-C154226-04)

References

  1. CCTV NEWS, URL: https://www.cctvnews.co.kr/news/articleView.html?idxno=209837
  2. Lindqvist, B., Mansouri, S. S., Agha-mohammadi, A. and Nikolakopoulos, G., "Nonlinear MPC for Collision Avoidance and Control of UAVs With Dynamic Obstacles," IEEE Robotics and Automation Letters, Vol. 5, No. 4, October 2020, pp. 6001~6008. https://doi.org/10.1109/LRA.2020.3010730
  3. Choe, D. G., "A Collision-Avoidance Navigation Algorithm for Heavy-Traffic Drone Networks," Sungkyunkwan University, April 2019.
  4. Damas, B. and Sntows-Victor, J., "Avoiding moving obstacles: the forbidden velocity map," IEEE, RSJ International Conference on Intelligent Robots and Systems, October 2009, pp. 4393~4398.
  5. Kim, I. H., "Obstacle avoidance and local path planning for mobile robots using the fast elastic band," Journal of Institute of Control, Robotics and Systems, Vol. 16, No. 8, August 2010, pp. 794~798. https://doi.org/10.5302/J.ICROS.2010.16.8.794
  6. Lee, J. G., Kim, J. H., Kang, T. Y. and Ryoo, C. K., "Multi-UAV Path Planning With Velocity Optimization Based on Voronoi Diagram," Proceeding of The Korean Society for Aeronautical and Space Sciences Spring Conference, July 2021, pp. 165~166.
  7. Bae, S. and Shin, H., "A New Graph-Based Flight Planning Algorithm for Unmanned Aircraft System Traffic Management," 2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC), 2018, pp. 1~9.
  8. Sartori, D., Zou, D. and Yu, W., "An efficient approach to near-optimal 3D trajectory design in cluttered environments for multirotor UAVs," 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), August 2019, pp. 1016~1022.
  9. Bae, S. J., "A New Multiple Flights Routing and Scheduling Algorithm in Terminal Manoeuvring Area," 2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC), 2018, pp. 1~9.
  10. Boyd, S. and Vandenberghe, L., "Convex Optimization," New York: Cambridge University Press, 2004.
  11. Nemirovski, A., "Interior Point Polynomial Time Methods in Convex Programming," 1996.
  12. SDPT3 Aps, "SDPT3 a matlab software package," Release-3.02, http://cvxr.com/cvx/beta/