Optimum Design of the Cooperation Objective for Computer Vision-Based UAV Autonomous Landing

Abstract:

In this paper, a novel approach to improve the infrared cooperative object for accurately landing is proposed. We present the designed cooperative object system and the blurring around the object when its working, and then begin our basic research on the cooperative object. We analyzed the formation principle of thermal blurring in the infrared imaging mechanism. After a detailed analysis infrared thermal blurring from thermodynamic principle, we derived the main factors of the infrared thermal blurring, and used Fluent simulation software to simulate the formation of thermal blurring. At the same time, the paper optimized the design of cooperation target to reduce thermal blurring. Experiments show that the optimum design of the cooperation objectives thermal blurring has significantly improved compared with some other methods.

• Periodical: Advanced Materials Research (Volumes 718-720)
• Pages: 1221-1227
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.718-720.1221
• Online since: July 2013
• Authors: Dong Wu Feng, Gui Li Xu, Biao Wang, Yu Peng Tian, Yong Qiang Ye
• Keywords: Autonomous Landing, Blurring, Computer Vision, Infrared

References

[1] Chitrakaran Vilas K, Dawson Darren M, Chen Jian, et al, 2005. Vision assisted autonomous landing of an unmanned aerial vehicle [A]. Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05[C], Seville, Spain, 1465- 1470.

DOI: 10.1109/cdc.2005.1582365

[2] Flusser J Kautsky, J Sroubek F, 2007. Object recognition by implicit invariants [A]. Computer Analysis of Images and Patterns. Proceedings 12th International Conference, CAIP 2007 [C], Vienna, Austria, 856-863.

DOI: 10.1007/978-3-540-74272-2_106

[3] Lemaitre Cedric, Miteran Johel, Aubreton Olivier, et.al, 2007. A hardware architecture for fast video object recognition using SVM and Zernike Moments [A]. Proceedings of SPIE - The International Society for Optical Engineering [C], Le Creusot, France, 63560X.

DOI: 10.1117/12.736745

[4] Marci Meingast, Christopher Geyer, Shankar Sastry, 2004.Vision based terrain recovery for landing unmanned aerial vehicles [A]. 2004 43rd IEEE Conference on Decision and Control (CDC) [C], Nassau, Bahamas, 1670-1675.

DOI: 10.1109/cdc.2004.1430284

[5] Milan Sonka, Vaclav Hlavac, Roger Boyle, 2003. Image processing, analysis and machine vision [M]. Posts and Telecommunications News Press, Beijing.

DOI: 10.1117/12.256634

[6] Mukherji, Shyamalee, 2005. Fast algorithms for binary cross-correlation [A]. IGARSS 2005 IEEE International Geoscience and Remote Sensing Symposium[C],Seoul, South Korea, 340-343.

DOI: 10.1109/igarss.2005.1526177

[7] G.L.Xu, Y.H. Chen. Key technology of unmanned aerial vehicle's navigation and automatic landing in all weather based on the cooperative object and infrared computer vision. Acta Aeronautica et Astronautica Sinica,2008,29(2):437-442.

[8] G.L. Xu, Y Zhang. Research on computer vision-based for UAV autonomous landing on a ship. Pattern Reconition Letters,2009.30(6):600-605.

DOI: 10.1016/j.patrec.2008.12.011