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Dual-channel LIDAR searching, positioning, tracking and landing system for rotorcraft from ships at sea

Published online by Cambridge University Press:  01 July 2022

Tao Zeng ,
Hua Wang ,
Xiucong Sun Open the ORCID record for Xiucong Sun [Opens in a new window] ,
Hui Li ,
Zhen Lu ,
Feifei Tong ,
Hao Cheng ,
Canlun Zheng  and
Mengying Zhang
Tao Zeng
Affiliation:
School of Astronautics, BeiHang University, Beijing, China Xi'an Institute of Electromechanical Information Technology, Xi'an, China
Hua Wang
Affiliation:
School of Astronautics, BeiHang University, Beijing, China
Xiucong Sun*
Affiliation:
School of Astronautics, BeiHang University, Beijing, China
Hui Li
Affiliation:
Henan Costar Group Co. Ltd, Nanyang, China
Zhen Lu
Affiliation:
Xi'an Institute of Electromechanical Information Technology, Xi'an, China
Feifei Tong
Affiliation:
Henan Costar Group Co. Ltd, Nanyang, China
Hao Cheng
Affiliation:
School of Astronautics, BeiHang University, Beijing, China
Canlun Zheng
Affiliation:
School of Astronautics, BeiHang University, Beijing, China
Mengying Zhang
Affiliation:
Bejing Institute of Electronic System Engineering, Beijing, China
*
*Corresponding author. E-mail: xiucong.sun@buaa.edu.cn
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Abstract

To address the shortcomings of existing methods for rotorcraft searching, positioning, tracking and landing on a ship at sea, a dual-channel LIDAR searching, positioning, tracking and landing system (DCLSPTLS) is proposed in this paper, which utilises the multi-pulse laser echoes accumulation method and the physical phenomenon that the laser reflectivity of the ship deck in the near-infrared band is four orders of magnitude higher than that of the sea surface. The DCLSPTLS searching and positioning model, tracking model and landing model are established, respectively. The searching and positioning model can provide estimates of the azimuth angle, the distance of the ship relative to the rotorcraft and the ship's course. With the above parameters as inputs, the total tracking time and the direction of the rotorcraft tracking speed can be obtained by using the tracking model. The landing model can calculate the pitch and the roll angles of the ship's deck relative to the rotorcraft by using the least squares method and the laser irradiation coordinates. The simulation shows that the DCLSPTLS can realise the functions of rotorcraft searching, positioning, tracking and landing by using the above parameters. To verify the effectiveness of the DCLSPTLS, a functional test is performed using a rotorcraft and a model ship on a lake. The test results are consistent with the results of the simulation.

Keywords

dual-channel LIDARsearchingpositioningtrackinglanding
Type
Research Article
Information
The Journal of Navigation , Volume 75 , Issue 4 , July 2022 , pp. 901 - 927
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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References

Alizadeh, D., Alesheikh, A. A. and Sharif, M. (2021). Vessel trajectory prediction using historical automatic identification system data. The Journal of Navigation, 74, 156–174.CrossRefGoogle Scholar
Arora, S., Jain, S., Scherer, S., Nuske, S., Chamberlain, L. and Singh, S. (2013). Infrastructure-free shipdeck tracking for autonomous landing. Proceedings of the International Conference on Robotics and Automation, 323–330. doi: 10.1109/ICRA.2013.6630595.Google Scholar
Chaves, S. M., Wolcott, R. W. and Eustice, R. M. (2015). NEEC research: toward GPS-denied landing of unmanned aerial vehicle on ships at sea. Naval Engineers Journal, 127(1), 23–35.Google Scholar
Garratt, M., Pota, H., Lambert, A., Eckersley-Maslin, S. and Farabet, C. (2009). Visual tracking and lidar relative positioning for automated launch and recovery of an unmanned rotorcraft from ships at sea. Naval Engineers Journal, 121(2), 99–110.CrossRefGoogle Scholar
Gold, K. and Brown, A. (2004). A Hybrid Integrity Solution for Precision Landing and Guidance. Position Location and Navigation Symposium, 2004. PLANS 2004, April 2004, 165–174.Google Scholar
Kelly, J. (2013). Unmanned X-47B readies for final touchdown. July. [Online]. Available at: http://navylive.dodlive.mil/2013/07/09/unmannedx-47b-readies-for-final-touchdown/.Google Scholar
Liu, S. F., Atia, M. M., Karamat, T. B. and Noureldin, A. (2015). A LiDAR-aided indoor navigation system for UGVs. The Journal of Navigation, 68, 253–273.CrossRefGoogle Scholar
Liu, W. W., Liu, Y. C., Gunawan, B. A. and Bucknall, R. (2021). Practical moving target detection in maritime environments using fuzzy multi-sensor data fusion. International Journal of Fuzzy Systems, 23, 1860–1878.CrossRefGoogle Scholar
Liu, R. W., Guo, Y., Nie, J. T., Hu, Q., Xiong, Z. H., Yu, H. and Guizani, M. (2022). Intelligent edge-enabled efficient multi-source data fusion for autonomous surface vehicles in maritime Internet of Things. IEEE Transactions on Green Communications and Networking, 1–14. DOI: 10.1109/TGCN.2022.3158004CrossRefGoogle Scholar
Pervan, B., Chan, F.-C., and Colby, G. (2003). Performance analysis of carrier-phase DGPS navigation for shipboard landing of aircraft. Navigation, 50(3), 181–191.CrossRefGoogle Scholar
Richardson, T. S., Jones, C. G., Likhoded, A., Sparks, E., Jordan, A., Cowling, L. and Willcox, S. (2013). Automation vision-based recovery of a rotary wing unmanned aerial vehicle onto a moving platform. Journal of Field Robotics, 30(5), 667–684.CrossRefGoogle Scholar
Sabatini, R. and Richardson, M. A. (2003). A new approach to eye-safety analysis for airborne laser systems flight test and training operations. Optics & Laser Technology, 35(2), 191–198.CrossRefGoogle Scholar
Sabatini, R., Richardson, M. A., Jia, H. and Zammit-Mangion, D. (2012). Airborne Laser Systems for Atmospheric Sounding in the Near Infrared. SPIE Laser Sources and Applications, Photonics Europe 2012, vol. 8433. Brussels, Belgium, SPIE.CrossRefGoogle Scholar
Sierra Nevada Corporation (2008). UCARS-V2 UAS common automatic recovery system - version2. [Online]. Available at: https://www.sncorp.com/media/3218/ucars-v2_uav-common-automatic-recovery-system-version-2-product-sheet.pdf.Google Scholar
Wen, J. X., Li, G. R. and Zhao, Y. H. (2022). Detection of abnormal ship trajectory based on the complex polygon. The Journal of Navigation, 1–18.Google Scholar
Xu, G., Zhang, Y., Ji, S., Cheng, Y. and Tian, Y. (2009). Research on computer vision-based for UAV autonomous landing on a ship. Pattern Recognition Letters, 30(6), 600–605.CrossRefGoogle Scholar
Yakimenko, O. A., Kaminer, I. I., Lentz, W. J. and Ghyzel, P. A. (2002). Unmanned aircraft navigation for shipboard landing using infrared vision. IEEE Transactions on Aerospace Electronic Systems, 38, 1181–1200.CrossRefGoogle Scholar
Zeng, T., Wang, H., Wei, W., Cheng, H., Zheng, C. L., Feng, X. Y. and Shan, W. (2021). Four-channel LIDAR relative navigation system for rocket first stage recovery at sea. Computer Communications, 175, 123–141.CrossRefGoogle Scholar
Zhong, S. Y. and Li, S. S. (2006). Study of multi-pulsed laser ranging technology. Laser and Infrared, 36(9), 797–799.Google Scholar
Zhou, F., Chen, H. and Zhang, P. (2020). Performance evaluation of maritime search and rescue missions using automatic identification system data. The Journal of Navigation, 73, 1237–1246.CrossRefGoogle Scholar