Heuristic Search Based on State Transition Graphs for Deep Space Task Planning

doi:10.15982/j.issn.2095-7777.2019.04.008

PDF(987 KB)

Journal of Deep Space Exploration ›› 2019, Vol. 6 ›› Issue (4) : 364-368. DOI: 10.15982/j.issn.2095-7777.2019.04.008

Topic: Autonomous Control for Spacecraft

Heuristic Search Based on State Transition Graphs for Deep Space Task Planning

JIN Hao^1,2, XU Rui^1,2, CUI Pingyuan^1,2, ZHU Shengying^1,2

Author information +

History +

Abstract

In view of the complex system and coupling operation constraints of deep space probes,state transition graphs are defined based on the timeline knowledge representation. With the analysis of involved constraints in task planning,the computation procedure of cost estimate for state transition is designed. In addition,the state transition graph based heuristic planning algorithm is proposed and is able to prune irrelevant search space,and accelerate the searching process. Simulation results indicate that the algorithm can reduce unnecessary planning steps and make certain improvements in planning efficiency.

Keywords

task planning / heuristic search / state transition graph

Cite this article

EndNote

Ris (Procite)

Bibtex

Download citation ▾

JIN Hao, XU Rui, CUI Pingyuan, ZHU Shengying. Heuristic Search Based on State Transition Graphs for Deep Space Task Planning. Journal of Deep Space Exploration, 2019, 6(4): 364‒368 https://doi.org/10.15982/j.issn.2095-7777.2019.04.008

References

[1] 崔平远, 徐瑞, 朱圣英, 等. 深空探测器自主技术发展现状与趋势[J]. 航空学报, 2014, 35(1):13-28. CUI P Y, XU R, ZHU S Y, et al. The research status and developing tends of on-board autonomy technology for deep space explorer[J]. Acta Aeronautica ET AstronauticaSinica, 2014, 35(1):13-28.
[2] MCINTOSH T, MULHEARN T, GIBSON C, et al. Planning for long-duration space exploration:interviews with NASA subject matter experts[J]. Acta Astronautica, 2016(129):477-487.
[3] LIN K P, LUO Y Z, ZHANG J, et al. Space station overall mission planning using decomposition approach[J]. Aerospace Science & Technology, 2014, 33(1):26-39.
[4] BARREIRO J, BOYCE M, DO M, et al. EUROPA:a platform for AI planning, scheduling, constraint programming, and optimization[C]//The 4th International Competition on Knowledge Engineering for Planning and Scheduling.Toronto, Canada:[s.n], 2012.
[5] VALLAT C, ALTOBELLI N, GEIGER B, et al. The science planning process on the Rosetta mission[J]. Acta Astronautica, 2017(133):244-257.
[6] MOUSSI A, FRONTON J F, GAUDON P, et al. The Philae lander:Science planning and operations[J]. Acta Astronautica, 2016(125):92-104.
[7] 王丹, 徐进, 陈丹.基于自主规划的载人航天器飞行程序设计[J].航天器工程, 2015, 24(1):50-55. WANG D, XU J, CHEN D. Design of flight procedure based on autonomous planning for manned spacecraft[J]. Spacecraft Engineering, 2015, 24(1):50-55
[8] 王晓晖, 李爽. 深空探测器约束简化与任务规划方法研究[J]. 宇航学报, 2016, 37(7):768-774. WANG X H, LI S. Research on constraint simplification and mission planning method for deep space explorer[J]. Acta Aeronautica ET Astronautica Sinica, 2016, 37(7):768-774.