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Artificial Neural Network Based Mission Planning Mechanism for Spacecraft

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

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.

Keywords

Deep space exploration Artificial neural networks Autonomous control Mission planning 

Notes

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).

References

  1. 1.
    Gomez M (2003) A typical spacecraft autonomy system. IMCL workshop on machine learning for autonomous space applicationsGoogle Scholar
  2. 2.
    Cui PY, Xu R, Zhu SY, Zhao FY (2014) State of the art and development trends of on-board autonomy technology for deep space explorer. Acta Aeronaut Astronaut Sin 35(1):13–28.  https://doi.org/10.7527/S1000-6893.2013.0335 Google Scholar
  3. 3.
    Xu R, Cui PY, Xu XF et al (2004) Timeline based autonomous mission planning system for deep space exploration. J Harbin Inst Technol (New Ser) 11(1):60–66.  https://doi.org/10.3969/j.issn.1005-9113.2004.01.013 Google Scholar
  4. 4.
    Ghallab M, Nau D, Traverso P (2004) Automated planning: theory and practice. Morgan Kaufmann, BurlingtonzbMATHGoogle Scholar
  5. 5.
    Bonet B, Loerincs G, Geffner H (1997) A robust and fast action selection mechanism for planning. In: Proceedings of the 1997 14th national conference on artificial intelligence, ProvidenceGoogle Scholar
  6. 6.
    Chern CC, Chen YL, Kung LC (2010) A heuristic relief transportation planning algorithm for emergency supply chain management. Int J Comput Math 87(7):1638–1664.  https://doi.org/10.1080/00207160802441256 CrossRefzbMATHGoogle Scholar
  7. 7.
    Barreiro J, Boyce M, Do M et al (2012) EUROPA: a platform for ai planning, scheduling, constraint programming, and optimization. The 4th international competition on knowledge engineering for planning and scheduling (ICKEPS). Atibaia, Sao PauloGoogle Scholar
  8. 8.
    Fukunaga A, Rabideau G, Chien S et al (1997) ASPEN: a framework for automated planning and scheduling of spacecraft control and operations. In: Proceedings of international symposium on AI, robotics and automation in space, TokyoGoogle Scholar
  9. 9.
    Laborie P, Ghallab M (1995) IxTeT: an integrated approach for plan generation and scheduling. In: 1995 INRIA/IEEE symposium on emerging technologies and factory automation, ETFA ’95, vol 1, pp 485-495.  https://doi.org/10.1109/etfa.1995.496801
  10. 10.
    Bedrax-Weiss T, McGann C, Iatauro M (2005) EUROPA2: plan database services for planning and scheduling applications. ICAPS 2005 workshop of system demonstration, MontereyGoogle Scholar
  11. 11.
    Xie JH (2008) Parallel bionic algorithm used to optimize sensor placement for self-diagnostic smart structures. International conference on computer science and information technology, Singapore.  https://doi.org/10.1109/iccsit.2008.29
  12. 12.
    Kriesel D (2010) A brief introduction to neural networks. http://www.dkriesel.com/en/science/neural_networks
  13. 13.
    Dayhoff JE, DeLeo JM (2001) Artificial neural networks. Cancer 91(S8):1615–1635.  https://doi.org/10.1002/1097-0142(20010415)91:8+%3c1615::AID-CNCR1175%3e3.0.CO;2-L CrossRefGoogle Scholar
  14. 14.
    Hornik K, Stinchcomb X, White X (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359–366.  https://doi.org/10.1016/0893-6080(89)90020-8 CrossRefzbMATHGoogle Scholar
  15. 15.
    Li H, Yang SX, Seto ML (2009) Neural-network-based path planning for a multirobot system with moving obstacles. IEEE Trans Syst Man Cybern Part C Appl Rev 39(4):410–419.  https://doi.org/10.1109/coase.2008.4626446 CrossRefGoogle Scholar
  16. 16.
    Akira A (2011) Trajectory planning for flexible Cartesian robot manipulator by using artificial neural network: numerical simulation and experimental verification. Robotica 29:797–804.  https://doi.org/10.1017/s0263574710000767 CrossRefGoogle Scholar
  17. 17.
    Chen JJ, Zhao P, Liang HW, Mei T (2014) Motion planning for autonomous vehicle based on radial basis function neural network in unstructured environment. Sensors 14(9):17548–17566.  https://doi.org/10.3390/s140917548 CrossRefGoogle Scholar
  18. 18.
    Ahmed SU, Faraz K, Mazhar I (2014) Guided autowave pulse coupled neural network (GAPCNN) based real time path planning and an obstacle avoidance scheme for mobile robots. Robot Auton Syst 62(4):474–486.  https://doi.org/10.1016/j.robot.2013.12.004 CrossRefGoogle Scholar
  19. 19.
    Feng BM, Ma GC, Xie WN, Wang CH (2006) Robust tracking control of space robot via neural network. In: First international symposium on systems and control in aerospace and astronautics, Harbin.  https://doi.org/10.1109/isscaa.2006.1627472
  20. 20.
    Bassil Y (2012) Neural network model for path-planning of robotic rover systems. Int J Sci Technol 2(2):94–100Google Scholar
  21. 21.
    Glasius R, Komoda A, Gielen S (1995) Neural network dynamics for path planning and obstacle avoidance. Neural Netw 8(1):125–133.  https://doi.org/10.1016/0893-6080(94)e0045-m CrossRefGoogle Scholar
  22. 22.
    Yang SX, Meng M (2000) An efficient neural network approach to dynamic robot motion planning. Neural Netw 13(2):143–148.  https://doi.org/10.1016/s0893-6080(99)00103-3 CrossRefGoogle Scholar
  23. 23.
    Kassim AA, Vijaya Kumar B (1997) The wave expansion neural network. Neurocomputing 16(3):237–258.  https://doi.org/10.1016/s0925-2312(97)00034-9 CrossRefGoogle Scholar
  24. 24.
    Wang CH, Feng BM, Ma GC, Ma C (2005) Robust tracking control of space robots using fuzzy neural network. In: Proceedings of IEEE international symposium on computational intelligence in robotics and automation, Espoo.  https://doi.org/10.1109/cira.2005.1554274
  25. 25.
    Jolly KG, Kumar S, Vijayakumar R (2010) Intelligent task planning and action selection of a mobile robot in a multi-agent system through a fuzzy neural network approach. Eng Appl Artif Intell 23(6):923–933.  https://doi.org/10.1016/j.engappai.2010.04.001 CrossRefGoogle Scholar
  26. 26.
    Smith B, Millar W, Dunphy J et al (1999) Validation and verification of the remote agent for spacecraft autonomy. In: Proceedings of IEEE aerospace conference. Snowmass at Aspen, CO.  https://doi.org/10.1109/aero.1999.794352
  27. 27.
    Victoria J, Policella N, Gao Y, Stryk O (2012) Design concepts for a new temporal planning paradigm. In: International conference on automated planning and scheduling—workshop on planning and scheduling with timelinesGoogle Scholar
  28. 28.
    Corsten H, May C (1996) Artificial neural networks for supporting production planning and control. Technovation 16(2):67–93.  https://doi.org/10.1016/0166-4972(95)00024-0 CrossRefGoogle Scholar
  29. 29.
    Stottler R, Breeden D (2012) Incorporating high-speed, optimizing scheduling into NASA’s EUROPA planning architecture. Infotech @ Aerospace, Garden Grove.  https://doi.org/10.2514/6.2012-2406
  30. 30.
    Cimatti A, Micheli A, Roveri M (2013) Timelines with temporal uncertainty. In: Proceedings of the 27th AAAI conference on artificial intelligence, BellevueGoogle Scholar
  31. 31.
    Ai-Chang M, Bresina J, Charest L et al (2004) MAPGEN planner: mixed-initiative activity planning for the mars exploration Rover Mission. IEEE Intell Syst 19(1):8–12.  https://doi.org/10.1109/mis.2004.1265878 CrossRefGoogle Scholar

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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