Path planning based on ant colony and genetic fusion algorithm for communication supporting spacecraft

SUN Zhao-wei1; LIU Xue-kui1; WU Xian-de2; DENG Hong1

doi:10.3788/OPE.20132112.3308

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Path planning based on ant colony and genetic fusion algorithm for communication supporting spacecraft

更新时间：2020-08-12

- Path planning based on ant colony and genetic fusion algorithm for communication supporting spacecraft
- Optics and Precision Engineering Vol. 21, Issue 12, Pages: 3308-3316(2013)
- 作者机构：
  
  1. 哈尔滨工业大学卫星技术研究所，黑龙江哈尔滨 150080
  2. 哈尔滨工程大学航天与建筑工程学院，黑龙江哈尔滨 150001
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20132112.3308
  CLC： TP391;V412.4+1
- Received：29 August 2013，
  
  Revised：13 September 2013，
  
  Published Online：25 December 2013，
  
  Published：25 December 2013
- 稿件说明：
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孙兆伟, 刘雪奎, 吴限德, 邓泓. 用于通信保障航天器遗传蚁群融合路径规划[J]. 光学精密工程, 2013,21(12): 3308-3316

SUN Zhao-Wei, LIU Xue-Kui, TUN Xian-De, DENG Hong. Path planning based on ant colony and genetic fusion algorithm for communication supporting spacecraft[J]. Editorial Office of Optics and Precision Engineering, 2013,21(12): 3308-3316
孙兆伟, 刘雪奎, 吴限德, 邓泓. 用于通信保障航天器遗传蚁群融合路径规划[J]. 光学精密工程, 2013,21(12): 3308-3316 DOI： 10.3788/OPE.20132112.3308.

SUN Zhao-Wei, LIU Xue-Kui, TUN Xian-De, DENG Hong. Path planning based on ant colony and genetic fusion algorithm for communication supporting spacecraft[J]. Editorial Office of Optics and Precision Engineering, 2013,21(12): 3308-3316 DOI： 10.3788/OPE.20132112.3308.

摘要

为实现通信保障航天器的大范围轨道机动，提出了融合遗传和蚁群算法的通信保障航天器机动路径规划方法。首先，建立了空间飞行器的轨道运动模型，在此基础上研究了遗传算法和蚁群算法的融合策略，利用遗传算法在初期的快速收敛性以及蚁群算法在后期的高效性，分别设计了融合算法中的遗传算法和蚁群算法，并给出融合算法的运行过程。最后，对本文提出的路径规划进行了仿真验证。结果表明：融合算法完成100次迭代后，目标函数即可达到稳定区间；安全路径平均长度为4.055 3104 m。结果验证了基于遗传-蚁群融合算法的机动轨道规划具有更快的收敛速度，能够在更短的时间内规划出到达故障航天器的飞行路径，显著提高了规划效率。

Abstract

To implement the orbit maneuvering of a communication supporting spacecraft in a large area

the path planning of the communication supporting spacecraft was proposed based on the fusion of ant colony and genetic algorithms. Firstly

the orbit dynamic model of spacecraft was established

and the fusion scheme of the genetic and ant colony algorithms was studied. On the basis of the fast convergence of genetic algorithm and the higher efficiency of colony algorithm

the genetic and ant colony algorithms were redesigned respectively under a fusion framework

and the operation process was presented. Finally

the path planning was simulated. The simulation results show that the fusion algorithm can reach the stabilization after 100 iterations

and the average safety path length is 4.055 3104 m. These results prove that the convergence speed of maneuvering orbit planning based on genetic and ant colony fusion is fast

and the flight path to a fault of spacecraft can be obtained in a shorter time

which raises the efficiency of orbit planning remarkably. Key words: path planning; genetic algorithm; ant colony algorithm; orbital maneuvering; communication supporting spacecraft

关键词

Keywords

references

［1］ELLERY A，KREISEL J，SOMMER B. The case for robotic on-orbit servicing of spacecraft: Spacecraft reliability is a myth ［J］. Acta Astronautica, 2008, 63(5-6):632-648.［2］崔乃刚，王平，郭继峰，等. 空间在轨服务技术发展综述［J］. 宇航学报，2007，28(4)：805-811.CUI N G,WANG P,GUO J F, et al.. A review of on-orbit servicing ［J］. Journal of Astronautics,2007, 28(4):805-811.(in Chinese)［3］梁斌，徐文福，李成，等. 地球静止轨道在轨服务技术研究现状与发展趋势［J］. 宇航学报，2010，31(1)：1-13. LIANG B, XU W F, LI CH, et al.. The status and prospect of orbital servicing in the geostationary orbit ［J］. Journal of Astronautics,2010,31(1):1-13. (in Chinese)［4］XU W F, LIANG B, LI B, et al.. A universal on-orbit servicing system used in the geostationary orbit ［J］. Advances In Space Research, 2011, 48(1):95-119.［5］AL-JARRAH M A,HASAN M M. HILS setup of dynamic flight path planning in 3D environment with flexible mission planning using Ground Station［J］. Journal of the Franklin Institute, 2011,348(1):45-65.［6］KALA R,SHUKLA A,TIWARI R. Robotic path planning in static environment using hierarchical multi-neuron heuristic search and probability based fitness ［J］. Neurocomputing, 2011, 74(14-15): 2314-2335. ［7］ELSHAMLI A,ABDULLAH H A,AREIBI S. Genetic algorithm for dynamic path planning ［C］. Canadian Conference on Electrical and Computer Engineering, Sheraton Fallsview, Niagara Falls, Ontario, Canada, CCECE 2004.［8］MEI H,TIAN Y T, ZU L N. A hybrid ant colony optimization algorithm or path planning of robot in dynamic environment ［J］. International Journal of Information Technology, 2006,12(3): 78-88. ［9］周申培，严新平. 遗传蚁群融合算法及在不确定性无功优化中的应用研究［J］. 电力系统保护与控制， 2010，38（24）:120-123.ZHOU SH P，YAN X P. The fusion algorithm of genetic and ant colony and its application in uncertain reactive power optimization ［J］. Power System Protection and Control, 2010,38(24):120-123.［10］LEE Z J,SU SH F,CHUANG CH CH, et al.. Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment ［J］. Applied Soft Computing, 2008, 8(1):55-78.［11］章仁为. 卫星轨道姿态动力学与控制［M］. 北京: 北京航空航天大学出版社, 2006.ZHANG R W. Satellite Orbit Attitude Dynamics and Control［M］. Beijing: BUAA Press,2006.［12］AJMAL ALI M S,BABU N R,VARGHESE K. Collision free path planning of cooperative crane manipulators using genetic algorithm ［J］. Journal of Computing in Civil Engineering, 2005, 16(2): 182-193.［13］MULLEN R J,MONEKOSSO D,BARMAN S, et al.. A review of ant algorithms ［J］. Expert Systems with Application, 2009, 36(6): 9608-9617.［14］李璟璟, 张迎春, 李化义, 等. ASUKF方法在航天器自主导航中的应用［J］.哈尔滨工程大学学报,2011, 32(5):575-580.LI J J,ZHANG Y CH，LI H Y, et al.. Application of ASUKF in autonomous spacecraft navigation ［J］. Journal of Harbin Engineering University, 2011, 32(5):575-580.

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