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A spacecraft attitude manoeuvre planning algorithm based on improved policy gradient reinforcement learning

Published online by Cambridge University Press:  14 December 2021

Bing Hua ,
Shenggang Sun ,
Yunhua Wu  and
Zhiming Chen
Bing Hua*
Affiliation:
School of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Shenggang Sun
Affiliation:
School of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Yunhua Wu
Affiliation:
School of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Zhiming Chen
Affiliation:
School of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, China
*
*Corresponding author: E-mail: [email protected]
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Abstract

To solve the problem of spacecraft attitude manoeuvre planning under dynamic multiple mandatory pointing constraints and prohibited pointing constraints, a systematic attitude manoeuvre planning approach is proposed that is based on improved policy gradient reinforcement learning. This paper presents a succinct model of dynamic multiple constraints that is similar to a real situation faced by an in-orbit spacecraft. By introducing return baseline and adaptive policy exploration methods, the proposed method overcomes issues such as large variances and slow convergence rates. Concurrently, the required computation time of the proposed method is markedly reduced. Using the proposed method, the near optimal path of the attitude manoeuvre can be determined, making the method suitable for the control of micro spacecraft. Simulation results demonstrate that the planning results fully satisfy all constraints, including six prohibited pointing constraints and two mandatory pointing constraints. The spacecraft also maintains high orientation accuracy to the Earth and Sun during all attitude manoeuvres.

Keywords

spacecraft attitude manoeuvreattitude constraintspath planningreinforcement learningpolicy gradient
Type
Research Article
Information
The Journal of Navigation , Volume 75 , Issue 3 , May 2022 , pp. 662 - 684
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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