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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2021, Vol. 42 ›› Issue (2): 324149-324149.doi: 10.7527/S1000-6893.2020.24149

• Electronics and Electrical Engineering and Control • Previous Articles     Next Articles

Real-time motion prediction of space tumbling targets based on machine learning

YU Min1,2, LUO Jianjun1,2, WANG Mingming1,2   

  1. 1. Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518057, China;
    2. School of Astronautics, National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi'an 710072, China
  • Received:2020-04-27 Revised:2020-05-20 Published:2020-07-06
  • Supported by:
    Science, Technology and Innovation Commission of Shenzhen Municipality (JCYJ20190806154412671); National Natural Science Foundation of China (12072269, 61973256, 61690211); Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University (CX202019)

Abstract: Based on supervised machine learning (ML), this paper addresses the motion prediction issue of space tumbling targets to provide reliable data of the target motion for space robots when they capture the target. Physics-based motion prediction methods find it hard to solve this problem due to their ideal modelling assumptions and constant requests for vision feedbacks. Hence, a purely data-driven learning-based method, named Sparse Pseudo-input Gaussian Process (SPGP), is employed. Given observed data for the motion state of the space tumbling target, this method continuously optimizes the real data to obtain a sparse pseudo training dataset, making it feasible for a fast online motion prediction implementation with the computational time of prediction within milliseconds. Moreover, the Markov Chain Monte Carlo(MCMC) method is adopted for the continuous optimization, overcoming the local minima problem resulted from the random initial guess during the optimization process. Snelson's data is employed to validate the correctness of the proposed SPGP regression method, and several simulation cases are conducted to demonstrate its effectiveness and robustness.

Key words: supervised machine learning, data-driven, Sparse Pseudo-input Gaussian Process (SPGP), Markov Chain Monte Carlo(MCMC), space tumbling targets, motion prediction

CLC Number: