Artificial Neural Network Based Mission Planning Mechanism for Spacecraft

  • Zhaoyu Li
  • Rui Xu
  • Pingyuan Cui
  • Shengying Zhu
Original Paper


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.


Deep space exploration Artificial neural networks Autonomous control Mission planning 



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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Copyright information

© The Korean Society for Aeronautical & Space Sciences and Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Zhaoyu Li
    • 1
    • 3
  • Rui Xu
    • 2
  • Pingyuan Cui
    • 2
  • Shengying Zhu
    • 2
  1. 1.School of Aerospace EngineeringBeijing Institute of TechnologyBeijingChina
  2. 2.Ministry of Industry and Information TechnologyBeijingChina
  3. 3.Key Laboratory of Autonomous Navigation and Control for Deep Space ExplorationMinistry of Industry and Information TechnologyBeijingChina

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