Modeling of Probe-Drogue Docking Success Probability for UAV Autonomous Refuelling
|Xiangmin Wang1 , Jun Wang2|
1 School of Automation, Nanjing University of Science and Technology, Nanjing, China.
2 2011 Collaborative Innovation Center, Nanjing University of Science and Technology, Nanjing, China.
Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-6 , Page no. 1-6, Jun-2018
Online published on Jun 30, 2018
Copyright © Xiangmin Wang, Jun Wang . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
|View this paper at Google Scholar | DPI Digital Library|
|XML View||PDF Download|
IEEE Style Citation: Xiangmin Wang, Jun Wang, “Modeling of Probe-Drogue Docking Success Probability for UAV Autonomous Refuelling”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1-6, 2018.
MLA Style Citation: Xiangmin Wang, Jun Wang "Modeling of Probe-Drogue Docking Success Probability for UAV Autonomous Refuelling." International Journal of Computer Sciences and Engineering 6.6 (2018): 1-6.
APA Style Citation: Xiangmin Wang, Jun Wang, (2018). Modeling of Probe-Drogue Docking Success Probability for UAV Autonomous Refuelling. International Journal of Computer Sciences and Engineering, 6(6), 1-6.
|189||493 downloads||35 downloads|
|Docking process of UAV Autonomous Refueling is a critical issue during the docking phase of autonomous aerial refueling (AAR), and the successful docking between the probe and drogue need higher probability for an aerial refueling system. To cope with this issue, a novel and effective model based on the theory of stochastic process crossing target area is proposed. In order to ensure its accurate and easy application, according to prior information and assumptions for the movements of the probe related to the drogue, the probe-drogue docking success probability is converted to the probability of the probe located in the circle area of drogue. The temporal and spatial characteristics of the pointing error have been considered which makes the model of the docking success probability more accord with the actual situation. simulations were conducted to demonstrate the effectiveness of the proposed method. This model provides theoretical support for the design and verification of AAR’s control system.|
|Key-Words / Index Term :|
|Autonomous aerial refueling, UAV, stochastic process, docking success probability|
 J. L. Hansen, J. E. Murray, N. V. Campos, “The NASA Dryden AAR project: a flight test approach to an aerial refueling system” , Proceedings of the Collection of Technical Papers - AIAA Atmospheric Flight Mechanics Conference, pp. 477–495, Reston, Virginia, USA, August 2004.
 M. Q. Hu, P. Liu, X. Nie, R. X. Zhou, “Influence of air turbulence on the movement of hose-drogue”, Flight Dynamics, vol. 28, No. 5, pp. 20–23, 2010.
 M.B. Giri, P. kulkarni, S. Bullock, and A.Doshi, “Agricultural Environmental Sensing Application Using Wireless Sensor Network for Automated Drip Irrigation”, International Journalof Computer Sciences and Engineering, vol. 8(6), pp. 14–35, 2016.
 A.Omanakuttan,D. Sreedhar,A. Manoj and A. Achankunju, “GPS and GSM Based Engine Locking System Using Smart Password”, International Journalof Computer Sciences and Engineering, vol. 5(4), pp. 57–61, 2017
 L. Y. Zhang, H. Zhang, Y. Yang, L. Huang, “Dynamics modeling and simulation of docking process in aerial refueling”, Acta Aeronautica et Astronautica Sinica, vol. 33, No. 7, pp. 1347– 1354, 2012.
 J. Zhang, S. Z. Yuan, Q. Q. Gong, “Modeling and control of shaking motion of aerial refueling hose-drogue”, Journal of System Simulation, vol. 28, No. 2, pp. 388–395, 2016.
 J. P. Nalepka and J. L. Hinchman, “Automated aerial refueling: extending the effectiveness of UAVs”, Proceedings of the AIAA Modeling and Simulation Technologies Conference and Exhibit, San Francisco, Calif, USA, pp. 2005–6005.2005
 H. T. Wang, X. M. Dong, H. F. Dou, J. P. Xue, “Dynamic modeling and characteristics analysis of hose-paradrogue aerial refueling system”, Journal of Beijing University of Aeronautics and Astronautics, vol. 40, No. 1, pp. 92–98, 2014.
 P. R. Thomas, U. Bhandari, S. Bullock, T. S. Richardson, “Advances in air to air refuelling”, Progress in Aerospace Sciences, vol. 71, No. 3,pp. 14–35, 2014.
 K. Ro , J. W. Kamman, “Modeling and simulation of hose-paradrogue aerial refueling systems” ,Journal of Guidance, Control, and Dynamics, vol. 33, No. 1, pp. 53–63, 2010.
 H Wang, X Dong, J Xue, J Liu, “Dynamic modeling of a hose-drogue aerial refueling system and integral sliding mode backstepping control for the hose whipping phenomenon”, Chinese journal of aeronautics, Vol.42, No.6, pp.61-70, 2014.
 Z Liu, J Liu, W He, “Modeling and vibration control of a flexible aerial refueling hose with variable lengths and input constraint”, Automatica, Vol.77, Issue.3, pp.302-310, 2017.
 Rachita Dahama, Kevin W. Sowerby, Gerard B. Rowe, “Outage Probability Estimation for Licensed Systems”, IEEE 69th Vehicular Technology Conference, Barcelona, Spain ,2009
 A. Dogan, W. Blake, C. Haag, “Bow wave effect in aerial refueling: computational analysis and modeling”, Journal of Aircraft, vol. 50, no. 6, pp. 1856–1868, 2013.
 X. H. Dai, Z. B. Wei, Q. Quan, “Modeling and simulation of bow wave effect in probe and drogue aerial refueling”, Chinese Journal of Aeronautics, vol. 29, no. 2, pp. 448–461, 2016..
 John Valasek, Kiran Gunnam, Jennifer Kimmett, John L. Junkins, Declan Hughes, Monish D. Tandale. "Vision-Based Sensor and Navigation System for Autonomous Air Refueling", Journal of Guidance, Control, and Dynamics, Vol. 28, No. 5, pp. 979-989. 2005.
 Xufeng Wang, Jianmin Li, Xingwei Kong, Xinmin Dong, Bo Zhang, “An Approach to Mathematical Modeling and Estimation of Probe-Drogue Docking Success Probability for UAV Autonomous Aerial Refuelin”, International Journal of Aerospace Engineering, Vol.2017, pp.1-14, 2017.
 X. F. Wang, X. M. Dong, X. W. Kong, J. M. Li, B. Zhang, “Drogue detection for autonomous aerial refueling based on convolutional neural networks”, Chinese Journal of Aeronautics, vol. 30, no. 1, pp. 380–390, 2017.
 H. W. Xie, H. L. Wang, “Binocular vision-based short-range navigation method for autonomous aerial refueling”, Journal of Beijing University of Aeronautics and Astronautics, vol. 37, no. 2, pp. 206–209, 2011.
 P.R. Thomas, U. Bhandari, S. Bullock, T. S. Richardson, and J. L. Du Bois, “Advances in air to air refuelling”, Progress in Aerospace Sciences, vol. 71, pp. 14–35, 2014.