DOI QR코드

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상대속도를 고려한 포텐셜 필드 기반 군집 무인수상선의 대형 제어

A Formation Control of Swarm Unmanned Surface Vehicles Using Potential Field Considering Relative Velocity

  • 백승대 (HD한국조선해양 디지털플랫폼연구실) ;
  • 김민승 (국립한국해양대학교 조선해양시스템공학과) ;
  • 우주현 (국립한국해양대학교 조선해양시스템공학부)
  • Seungdae Baek (Digital Technology Research Institute, HD Korea Shipbuilding and Offshore Engineering) ;
  • Minseung Kim (Department of Naval Architecture and Ocean Systems Engineering, National Korea Maritime and Ocean University) ;
  • Joohyun Woo (Division of Naval Architecture and Ocean Systems Engineering, National Korea Maritime and Ocean University)
  • 투고 : 2024.05.08
  • 심사 : 2024.05.13
  • 발행 : 2024.06.20

초록

With the advancement of autonomous navigation technology in maritime domain, there is an active research on swarming Unmanned Surface Vehicles (USVs) that can fulfill missions with low cost and high efficiency. In this study, we propose a formation control algorithm that maintains a certain shape when multiple unmanned surface vehicles operate in a swarm. In the case of swarming, individual USVs need to be able to accurately follow the target state and avoid collisions with obstacles or other vessels in the swarm. In order to generate guidance commands for swarm formation control, the potential field method has been a major focus of swarm control research, but the method using the potential field only uses the position information of obstacles or other ships, so it cannot effectively respond to moving targets and obstacles. In situations such as the formation change of a swarm of ships, the formation control is performed in a dense environment, so the position and velocity information of the target and nearby obstacles must be considered to effectively change the formation. In order to overcome these limitations, this paper applies a method that considers relative velocity to the potential field-based guidance law to improve target following and collision avoidance performance. Considering the relative velocity of the moving target, the potential field for nearby obstacles is newly defined by utilizing the concept of Velocity Obstacle (VO), and the effectiveness and efficiency of the proposed method is verified through swarm control simulation, and swarm control experiments using a small scaled unmanned surface vehicle platform.

키워드

과제정보

이 논문은 i) 2020학년도 한국해양대학교 신진교수 정착연구 지원사업 연구비와, ii) 2024년도 정부(산업통상자원부)의 재원으로 한국산업기술 진흥원의 지원(P0017006, 2024년, 산업혁신인재성장지원사업)을 받아 수행된 연구임

참고문헌

  1. Balch, T. and Arkin, R.C., 1998. Behavior-based formation control for multirobot teams, IEEE transactions on robotics and automation, 14(6), pp.926-939. https://doi.org/10.1109/70.736776
  2. Barnes, L., Field, M. and Valavanis, K., 2007. Unmanned ground vehicle swarm formation control using potential fields, 2007 Mediterranean Conference on Control andAutomation. IEEE, 2007.
  3. Barnes, L., Alvis, W., Fields, M., Valavanis, K., and Moreno, W., 2006. Swarm formation control with potential fields formed by bivariate normal functions, 2006 14th Mediterranean Conference on Control and Automation. IEEE, 2006.
  4. Bibuli, M., Bruzzone, G., Caccia, M., Gasparri, A., Priolo, A., and Zereik, E., 2014. Swarm-based path-following for cooperative unmanned surface vehicles. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 228(2) pp.192-207. https://doi.org/10.1177/1475090213516108
  5. Borenstein, J. and Koren, Y., 1989. Real-time obstacle avoidance for fast mobile robots, IEEE Transactions on systems, Man, and Cybernetics 19(5) pp.1179-1187. https://doi.org/10.1109/21.44033
  6. Borenstein, J., and Koren, Y., 1991. The vector field histogram-fast obstacle avoidance for mobile robots, IEEE transactions on robotics and automation, 7(3), pp.278-288. https://doi.org/10.1109/70.88137
  7. Chakravarthy, A. and Ghose, D., 1998. Obstacle avoidance in a dynamic environment: A collision cone approach, IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 28(5), pp.562-574. https://doi.org/10.1109/3468.709600
  8. Cui, R., Ge, S.S., How, B.V.E. and Choo, Y.S., 2010. Leader-follower formation control of underactuated autonomous underwater vehicles, Ocean Engineering, 37(17-18), pp.1491-1502. https://doi.org/10.1016/j.oceaneng.2010.07.006
  9. Ge, S.S. and Cui, Y.J., 2002. Dynamic motion planning for mobile robots using potential field method, Autonomous robots 13.3 (2002), pp.207-222.
  10. Ghommam, J. and Saad, M., 2017. Adaptive leader-follower formation control of underactuated surface vessels under asymmetric range and bearing constraints, IEEE Transactions on Vehicular Technology, 67(2), pp. 852-865 https://doi.org/10.1109/TVT.2017.2760367
  11. Kania, E.B., 2019. Chinese military innovation in artificial intelligence. Testimony to the US-China Economic and Security Review Commission.
  12. Khatib, M., and Chatila, R., 1995. An extended potential field approach for mobile robot sensor-based motions, Proc. International Conference on Intelligent Autonomous Systems (IAS'4)., 1995
  13. Kim, J.W., 2020, Local path planning for autonomous vehicles based model predictive control using velocity obstacles potential field in emergency situation, M.S. Korea Institute of Science and Technology.
  14. Ko, N.Y. and Lee, H.B., 1996. Avoidability measure in moving obstacle avoidance problem and its use for robot motion planning, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS'96, Vol. 3
  15. Kuwata, Y., Wolf, M.T., Zarzhitsky, D and Huntsberger, T.L, 2013. Safe maritime autonomous navigation with COLREGS, using velocity obstacles, IEEE Journal of Oceanic Engineering, 39(1), pp.110-119. https://doi.org/10.1109/JOE.2013.2254214
  16. Leonard, N.E. and Fiorelli, E., 2001. Virtual leaders, artificial potentials and coordinated control of groups, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No. 01CH37228), IEEE, pp.2968-2973.
  17. Lewis, M.A. and Tan, K.H., 1997. High precision formation control of mobile robots using virtual structures. Autonomous robots, 4(4), pp.387-403. https://doi.org/10.1023/A:1008814708459
  18. Oh, Y.S., Park, J.H., Kim, J.H. and Huh, U.Y., 2011. Formation conatrol and obstacle avoidance of mobile robot for moving object tracking, Journal of Electrical Engineering and Technology(JEET), 60(4) pp.856-861. https://doi.org/10.5370/KIEE.2011.60.4.856
  19. Park, J.H. and Huh, U.Y., 2015. Obstacle avoidance of leader-follower robots based on potential field and flexible formation. The Transactions of the Korean Institute of Electrical Engineers. pp.1389-1390.
  20. Park, J.H., Lee, Y.J., Jung, J.D., Kang, M.J. Choi, H.T. and Choi J.W., 2021, Preliminary study of potential field based formation controlfor cooperative navigation of multiple autonomous surface vehicles, Institute of Control Robotics and Systems, pp.290-291.
  21. Rimon, E. and Koditschek, D.E., 1992. Exact robot navigation using artificial potential functions, IEEE Transactions on Robotics And Automation, 8(5), pp.501-518. https://doi.org/10.1109/70.163777
  22. Sadowska, A., den Broek, T. V., Huijberts, H., van de Wouw, N., Kostic, D., and Nijmeijer, H., 2011. A virtual structure approach to formation control of unicycle mobile robots using mutual coupling. International Journal of Control, 84(11), pp.1886-1902. https://doi.org/10.1080/00207179.2011.627686
  23. Son, N.S., Han, J.W., Pyo, C.S. and Park, K,R., 2020. On the sea surveillance and illegal ship control by using unmanned surface vehicle swarm. Society of Naval Architects of Korea, 2020.
  24. Sonnenburg, C.R. and Woolsey, C.A., 2013, Modeling, identification, and control of an unmanned surface vehicle. Journal of Field Robotics, 2013, 30(3), pp. 371-398. https://doi.org/10.1002/rob.21452
  25. Sun, Z., Zhang, G., Lu, Y. and Zhang, W., 2018. Leader-follower formation control of underactuated surface vehicles based on sliding mode control and parameter estimation. ISA transactions, 72, pp.15-24. https://doi.org/10.1016/j.isatra.2017.11.008
  26. Tak, M.H., Joo, Y.H., 2014. Formation control algorithm for swarm robots using virtual force. The Transactions of the Korean Institute of Electrical Engineers, 63(10), pp. 1428-1433. https://doi.org/10.5370/KIEE.2014.63.10.1428
  27. Tychonievich, L., Zaret, D., Mantegna, J., Evans, R., Muehle, E., and Martin, S., 1989. A maneuvering-board approach to path planning with moving obstacles, Proceedings of the 11th international joint conference on Artificial intelligence, Vol.2, pp.1017-1021.
  28. Ulrich, I. and Borenstein, J., 1998. VFH+: Reliable obstacle avoidance for fast mobile robots, Proceedings of the 1998 IEEE International Conference on Robotics and Automation (Cat. No. 98CH36146). Vol. 2.
  29. Woo, J.H. and Kim, N.K., 2020, Collision avoidance for an unmanned surface vehicle using deep reinforcement learning, Ocean Eng ineering, 199 (2020): 107001.
  30. Yu, Z., Bao, X. and Nonami, K., 2008, Course keeping control of an autonomous boat using low cost sensors. Journal of System Design and Dynamics, 2(1), pp.389-400. https://doi.org/10.1299/jsdd.2.389