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Artificial Neural Network Based Mission Planning Mechanism for Spacecraft

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A Correction to this article was published on 21 June 2018

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Abstract

The ability to plan and react fast in dynamic space environments is central to intelligent behavior of spacecraft. For space and robotic applications, many planners have been used. But it is difficult to encode the domain knowledge and directly use existing techniques such as heuristic to improve the performance of the application systems. Therefore, regarding planning as an advanced control problem, this paper first proposes an autonomous mission planning and action selection mechanism through a multiple layer perceptron neural network approach to select actions in planning process and improve efficiency. To prove the availability and effectiveness, we use autonomous mission planning problems of the spacecraft, which is a sophisticated system with complex subsystems and constraints as an example. Simulation results have shown that artificial neural networks (ANNs) are usable for planning problems. Compared with the existing planning method in EUROPA, the mechanism using ANNs is more efficient and can guarantee stable performance. Therefore, the mechanism proposed in this paper is more suitable for planning problems of spacecraft that require real time and stability.

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  • 21 June 2018

    The original version of this article unfortunately contained a mistake. The information regarding the authors’ affiliations was incorrect.

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Acknowledgements

The authors gratefully acknowledge the support of the National Natural Science Foundation of China (61773061), the Defense Industrial Technology Development Program (JCKY2016602C018) and the Civil Aerospace Technology Research Project of China (MYHT201705).

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Authors and Affiliations

  1. School of Aerospace Engineering, Beijing Institute of Technology, Beijing, 100081, China

    Zhaoyu Li

  2. Ministry of Industry and Information Technology, Beijing, 100081, China

    Rui Xu, Pingyuan Cui & Shengying Zhu

  3. Key Laboratory of Autonomous Navigation and Control for Deep Space Exploration, Ministry of Industry and Information Technology, Beijing, 100081, China

    Zhaoyu Li

Authors
  1. Zhaoyu Li
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  2. Rui Xu
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  3. Pingyuan Cui
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  4. Shengying Zhu
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Correspondence to Rui Xu.

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Cite this article

Li, Z., Xu, R., Cui, P. et al. Artificial Neural Network Based Mission Planning Mechanism for Spacecraft. JASS 19, 111–119 (2018). https://doi.org/10.1007/s42405-018-0006-6

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  • DOI: https://doi.org/10.1007/s42405-018-0006-6

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