Journal of the Korea Institute of Military Science and Technology (한국군사과학기술학회지)

The Korea Institute of Military Science and Technology (한국군사과학기술학회)
권호 표지
DOI QR코드

DOI QR Code

An Image-based Ship Attitude Estimation Method for Helicopter Landing

회전익 항공기의 착함을 위한 이미지 기반 함정 자세 예측

  • Geonha Park (Department Mechanical System Engineering, Republic of Korea Naval Academy) ;
  • Sunghoon Jung (Department Mechanical System Engineering, Republic of Korea Naval Academy)
  • 박건하 (해군사관학교 기계시스템공학과) ;
  • 정성훈 (해군사관학교 기계시스템공학과)
  • Received : 2024.07.24
  • Accepted : 2024.09.28
  • Published : 2024.12.05
Download PDF
⟨ Previous Next ⟩

Abstract

Landing a helicopter on a moving ship requires accounting for the ship's attitude. However, it is not only challenging to visually assess the ship's attitude, but it also creates illusions for the pilot, increasing the risk of accidents. In this study, we propose an image-based ship attitude estimation method to assist helicopter landings. The proposed method enhances landing safety by predicting the ship's heave, pitch, and roll using only helicopter-mounted optical devices and pre-trained deep learning models, without requiring communication with the ship. To implement this approach, we generated a dataset by simulating a virtual sea environment and ship motion. Using this data, we trained deep learning models to predict the ship's attitude based solely on images. Experimental results confirm the feasibility of the proposed method, with VGG-16 demonstrating particularly effective attitude prediction under simulated conditions.

Keywords

References

  1. H. Tak, "A Study on the Prevention of the Rotary-wing Aircraft Pilot's Spatial Disorientation," Master's Dissertation, Kongju National University, Korea, 2015. 
  2. S. Saripalli, "Vision-based autonomous landing of an helicopter on a moving target," In Proceedings of the AIAA Guidance, Navigation, and Control Conference, p. 5660, 2009. 
  3. T. H. Dinh, H. L. T. Hong, and T. N. Dinh, "State estimation in visual inertial autonomous helicopter landing using optimisation on manifold," arXiv preprint arXiv:1907.06247, 2019. 
  4. S. Scherer, L. Chamberlain, and S. Singh, "Autonomous landing at unprepared sites by a full-scale helicopter," Robotics and Autonomous Systems, Vol. 60, No. 12, pp. 1545-1562, 2012. 
  5. J. L. Sanchez-Lopez, J. Pestana, S. Saripalli and, P. Campoy, "An approach toward visual autonomous ship board landing of a VTOL UAV," Journal of Intelligent & Robotic Systems, Vol. 74, pp. 113-127, 2014. 
  6. Q. H. Truong, T. Rakotomamonjy, A. Taghizad, and J. M. Biannic, "Vision-based control for helicopter ship landing with handling qualities constraints," IFAC-PapersOnLine, Vol. 49, No. 17, pp. 118-123, 2016. 
  7. G. Cho, J. Choi, G. Bae, and H. Oh, "Autonomous ship deck landing of a quadrotor UAV using feed-forward image-based visual servoing," Aerospace Science and Technology, Vol. 130(107869), 2022. 
  8. B. Lee, V. Saj, M. Benedict, and D. Kalathil, "Intelligent vision-based autonomous ship landing of VTOL UAVs," Journal of the American Helicopter Society, Vol. 68, No. 2, pp. 113-126, 2023. 
  9. B. L. TRUSKIN, "Vision-based Deck Estimation for Autonomous Ship-board Landing," Master's Dissertation, The Pennsylvania State University, Pennsylvania, 2013. 
  10. A. Cho, et. al., "Sea Wave and Ship Motion Simulation for Shipboard Landing of a VTOL UAV," In Proceedings of the KSAS 2013 Fall Conference, pp. 1270-1275, 2013. 
  11. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014. 
  12. K. HE, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016. 
  13. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the inception architecture for computer vision," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818-2826, 2016. 
  14. J. Shi and C. Tomasi, "Good Features to Track," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 593-600, 1994.