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Guidance and Control of a Robot Capturing an Uncooperative Space Target

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Abstract

This paper presented a new method to guide and control a space robot for capturing an uncooperative target. The dynamic model of a target is unknown and estimated with the help of vision system. This methodology has three different steps. First, the feature points of a space target were extracted using the vision system, then the pose of the target (position and orientation) relative to the space robot was determined based on Homography method. Second, because of an unknown model of the target, the location of the center of mass is calculated using kinematic equations and Iterative Closest Point (ICP) algorithm. This would help tracking moving target. Third, a new Adaptive Unscented Kalman Filter (AUKF) was introduced to estimate the dynamic state vector (position, orientation, linear and angular velocities) of an arbitrary space target. The error in AUKF estimation was prevented from divergence by using Fuzzy Logic Adaptive System (FLAS). Finally, a new trajectory method for planning the end-effector velocities of the space robot arm was implemented based on the measurement information from the vision system and estimation a target state using AUKF. The results from simulation experiments were presented and discussed.

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References

  1. Li, Z., Wang, B., Liu, H.: Target capturing control for space robots with unknown mass properties: a self-tuning method based on gyros and cameras. In: Journal of Sensors (2016)

  2. Biondi, G., Mauro, S., Mohtar, T.: A geometric method for estimating space debris center of mass position and orbital parameters from features tracking. In: IEEE, Metrology for Aerospace (MetroAeroSpace) (2015)

  3. Jasiobedzki, P., Greenspan, M., Roth, G.: Determination and tracking for autonomous satellite capture. In: Proceeding of the 6th International Symposium on Artificial Intelligence and Robotics and Automation in Space: i-SAIRAS 2001, Canadian Space Agency, St Hubert (2001)

  4. Abraham, M., Jasiobedzki, P., Umasuthan, M.: Robust 3D vision for autonomous space robotic operations. In: Proceeding of the 6Th International Symposium on Artificial Intelligence and Robotics and Automation in Space: I-SAIRAS 2001, Canadian Space Agency, St-Hubert (2001)

  5. Allen, P.K., Timcenko, A., Yoshimi, B., Michelman, P.: Automated tracking and grasping of a moving object with a robotic hand-eye system. IEEE Trans. Robot. Autom. 9(2), 152–165 (1993)

    Article  Google Scholar 

  6. Biondi, G., Mauro, S., Mohtar, T., et al.: Feature-based estimation of space debris angular rate via compressed sensing and Kalman filtering. In: IEEE, Metrology for Aerospace (MetroAeroSpace) (2016)

  7. Hillenbrand, U., Lampariello, R.: Motion and parameter estimation of a free-floating space object from range data for motion prediction. In: Proceedings of i-SAIRAS. 8th International Symposium on Artificial Intelligence, Robotics and Automation in Space, Munich (2005)

  8. Li, C., Liang, B., Xu, W.: Autonomous trajectory planning of free-floating robot for capturing space target. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China (2006)

  9. Xu, W., Liang, B., Li, C., et al.: Autonomous target capturing of free-floating space robot: Theory and experiments. Robotica 27, 425–445 (2009)

    Article  Google Scholar 

  10. Hu, G., MacKunis, W., Gans, N., et al.: Homography-based visual servo control with imperfect camera calibration. IEEE Trans. Autom. Control 54(6), 1318–1324 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  11. Bouguet J.: Camera calibration toolbox for Matlab. In: [Online]. https://www.vision.caltech.edu/bouguetj/calib_doc

  12. Al-Isawi, M.M., Sasiadek, J.Z.: Pose estimation for mobile and flying robots via vision system. In: Proceedings of the CARO3 – 3rd Conference on Aerospace Robotics (2015)

  13. Faugeras, O., Lustman, F.: Motion and structure from motion in a piecewise planar environment. In: Int. Journal of Pattern Recognition and Artificial Intelligence, pp. 485–508 (1988)

  14. Siciliano, B., Sciavicco, L.: Robotics modelling, planning and control. Springer-Verlag, London (2009)

    MATH  Google Scholar 

  15. Sheinfeld, D., Rock, S.M.: Rigid body inertia estimation with applications to the capture of a tumbling satellite. In: Proceedings of the 19th AAS/AIAA Spaceflight Mechanics Meeting. Savannah Georgia, pp. 343–356 (2009)

  16. Benninghoff, H., Boge, T.: Rendezvous involving a non-cooperative, tumbling target - estimation of moments of inertia and center of mass of an unknown target. In: 25th International Symposium on Space Flight Dynamics, pp. 25 (2015)

  17. Walker, M., Sasiadek, J.Z.: Accurate pose determination for autonomous vehicle navigation. In: IEEE- Conference on Methods and Models in Automation and Robotics (2013)

  18. Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–256 (1992)

    Article  Google Scholar 

  19. Kalman, D.: A singularly valuable decomposition: The SVD of a matrix. In: The American University (2002)

  20. Simon, J., Julier, J.K.: Unscented filtering and nonlinear estimation. Proc. IEEE 92(3), 401–422 (2004)

    Article  Google Scholar 

  21. Wan, E.A., van der Merwe, R.: The unscented kalman filter for nonlinear estimation. In: The IEEE Adaptive Systems for Signal Processing, Communications, and Control Symposium, pp. 153-158 (2000)

  22. Sasiadek, J.Z., Wang, Q.: Low cost automation using INS/GPS data fusion for accurate positioning. Robotica 21, 255–261 (2003)

    Article  Google Scholar 

  23. Sasiadek, J.Z., Wang, Q., Zeremba, M. B.: Fuzzy adaptive kalman filtering for INS/GPS data fusion. In: IEEE International Symposium on Intelligent Control Proceedings, pp. 181–186 (2000)

  24. Dah-Jing J., Sheng-Hung W.: Adaptive fuzzy strong tracking extended Kalman filtering for GPS navigation. IEEE Sens. J. 7(5), 778–789 (2007)

    Article  Google Scholar 

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Correspondence to Malik M. A. Al-Isawi.

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Al-Isawi, M.M.A., Sasiadek, J.Z. Guidance and Control of a Robot Capturing an Uncooperative Space Target. J Intell Robot Syst 93, 713–721 (2019). https://doi.org/10.1007/s10846-018-0874-9

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  • DOI: https://doi.org/10.1007/s10846-018-0874-9

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