Stereo Vision-Based Relative Navigation Algorithm for Satellites Formation Flying
International Journal of Astrophysics and Space Science
Volume 2, Issue 1, February 2014, Pages: 1-5
Received: Nov. 25, 2013; Published: Jan. 10, 2014
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Xiaokui Yue, National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi'an, P. R. China
Haifeng Su, National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi'an, P. R. China; Institute of Flight Mechanics and Control, Universität Stuttgart, Germany
Jianping Yuan, National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi'an, P. R. China
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Space mission with multiple spacecraft formation is an important means to space operation. A new relative navigation algorithm based on stereo vision is developed aiming at high navigation precision requirement of spacecraft formation flying. It uses stereo vision camera attached on the tracking craft as measurement sensor, gets the relative location of target craft in the tracking craft body reference frame with imaging parallax. Relative motion equation is built in inertial frame, and discretized as the state equation of the system. Measurement information of stereo vision is used as measurement value, and the two-step filter relative navigation algorithm based on Kalman filter is designed to estimate relative navigation state in real time, and finally validated by simulation. The simulation results prove this algorithm can meet the relative navigation precision requirements of formation flying.
Formation Flying, Stereo Vision, Relative Navigation, Two-Step Filter
To cite this article
Xiaokui Yue, Haifeng Su, Jianping Yuan, Stereo Vision-Based Relative Navigation Algorithm for Satellites Formation Flying, International Journal of Astrophysics and Space Science. Vol. 2, No. 1, 2014, pp. 1-5. doi: 10.11648/j.ijass.20140201.11
Hablani H B, Tapper M, Dana- Bashian D. Guidance algorithms for autonomous rendezous of spacecraft with a target vehicle in circular orbit [R]. AIAA -2001 - 4393 ,2001.
Roberto Alonso, John L Crassidis, John L Junkins. Vision- Based Navigation for Formation Flying of Spacecraft[R]. AIAA Guidance, Navigation and Control Conference, Denver, CO, August 2000, AIAA- 2000-4439.
Junkins J L, Hughes, Wazni, et al., Vision-Based Navigation for Rendezvous, Docking and Proximity Operations[R]. 22nd Annual AAS Guidance and Control Conference, Breckenridge, CO, Feb. 1999, AAS 99-021.
Zhang Shuqin, Space Rendezvous Measurement Technique and Engineering Application, Beijing, China Astronautic Press, 2005, 17-20(in Chinese).
Grosso E, Sandini G, Tistarelli M. 3D Object Reconstruction using Stereo and Motion Systems, Man and Cybernetics, IEEE Transactions on. Nov.-Dec. 1989, 19(6), 1465 – 1476.
Bradley C, Kurada S. Industrial Inspection Employing a Three Dimensional Vision system and a Neural Network Classifier [R]. Communications, Computers, and Signal Processing, 1995. Proceedings. IEEE Pacific Rim Conference on.1995, 505 – 508.
Yingen Xiong,Quek F. Machine vision for 3D mechanical part recognition in intelligent manufacturing environments. Robot Motion and Control, 2002, 441 – 446.
Liu Jiayin, WANG Zhong li, JIA Yun de, Error analysis of binocular stereo vision system, Optical Technology, 2003, 29(3): 354-357(in Chinese).
Zhang Guangjun, Machine Vision, Beijing, Sicence Press, 2005, 99-105(in Chinese).
Bouabdallah S, Becker M, and Siegwart R. Autonomous Miniature Flying Robots: Coming Soon! Research, Development, and Results. IEEE Robotics & Automation Magazine, 2007; 14(3): 88-98.
Erginer B, and Altuğ E. Modeling and PD control of a Quadrotor VTOL vehicle. Proceedings of the 2007 IEEE Intelligent Vehicles Symposium. Istanbul, Turkey, June 13-15, 2007.
Kim J, Kang MS, Park S. Accurate Modeling and Robust Hovering Control of a Quadrotor VTOL Aircraft. Journal of Intelligent & Robotic Systems, 2010; 57: 9-26.
Yoon K J, Goo N S. Development of a Small Autonomous Flying Robot with Four-Rotor System. The 15th International Conference on Advanced Robotics. Tallinn, Estonia, June 20-23, 2011.
Hamel T, Mahony R, Lozano R, Ostrowski J. Dynamic Modelling and Configuration Stabilization for an X4-Flyer. Proceedings of the 15th Triennial IFAC World Congress. Barcelona, Spain, July, 2002.
Hoffmann G, Rajnarayan D G, Waslander S L, Dostal D, Jang J S, and Tomlin C J. The Stanford Testbed of Autonomous Rotorcraft for Multi Agent Control (STARMAC). Proceedings of the 23rd Digital Avionics Systems Conference. Salt Lake City, UT, November 2004.
Nice E B. Design of a Four Rotor Hovering Vehicle. Master’s Thesis, Cornell University, 2004.
Hoffmann G M, Huang H, Waslander S L, and Tomlin C J. Quadrotor Helicopter Flight Dynamics and Control: Theory and Experiment. Conference of the American Institute of Aeronautics and Astronautics, August 2007, Hilton Head, South Carolina.
Tayebi A, McGilvray S. Attitude Stabilization of a VTOL Quadrotor Aircraft. IEEE Transactions on Control Systems Technology. Vol. 14, No. 3, May 2006.
Kis L, Regula G and Lantos B. Design and Hardware-in-the-loop Test of the Embedded Control System of an Indoor Quadrotor Helicopter. International Workshop on Intelligent Solutions in Embedded Systems. July 2008.
Hughes P. Spacecraft Attitude Dynamics. New York: Wiley, 1986.
Fay G. Derivation of the Aerodynamic Forces for the Mesicopter Simulation. Stanford University, February 14, 2001.
Anderson J. Fundamentals of Aerodynamics. New York: McGraw-Hill Book Company, 2001; 20-26.
Bresciani T. Modelling, Identification and Control of a Quadrotor Helicopter. Master’s thesis, Lund University, 2008.
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