Fast Mission Plan Repair Method for Mars Rover Based on State Difference

CHEN Chao1,2, XU Rui1,2, LI Zhaoyu1,2

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PDF(1092 KB)
Journal of Deep Space Exploration ›› 2021, Vol. 8 ›› Issue (2) : 124-131. DOI: 10.15982/j.issn.2096-9287.2021.20200075
Topic:Autonomous Planning Technology for Deep Space Exploration
Topic:Autonomous Planning Technology for Deep Space Exploration

Fast Mission Plan Repair Method for Mars Rover Based on State Difference

  • CHEN Chao1,2, XU Rui1,2, LI Zhaoyu1,2
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Abstract

The uncertainty of Mars environment and the difficulty to predict the failure of electronic equipment will seriously affect the actual effect of the pre-designed plan of the rover on Martian surface,resulting in plan execution failure. To solve this problem,a fast mission plan repair method based on state difference is proposed based on the fact that there is long delay between the Mars rover and the ground station. This method constructs partial states at different times by extracting the key information from the existing plan,lying on the difference between the perception state and the necessary state of action execution. And then the rapid mission plan repair strategy for Mars rover based on the partial state is presented. In this strategy,to improve the efficiency of plan repair,the search space deletion method based on the state difference between the actual state and the partial state is proposed. Simulation results demonstrate that this method can not only improve the efficiency of mission plan repair,but also ensure the plan stability.

Keywords

plan repair / Mars rover / partial state / state difference

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CHEN Chao, XU Rui, LI Zhaoyu. Fast Mission Plan Repair Method for Mars Rover Based on State Difference. Journal of Deep Space Exploration, 2021, 8(2): 124‒131 https://doi.org/10.15982/j.issn.2096-9287.2021.20200075

References

[1] MAIMONE M W,LEGER P C,BIESIADECKI J J. Overview of the Mars exploration rovers' autonomous mobility and vision capabilities[C]//IEEE International Conference on Robotics and Automation. Rome,Italy:Space Robotics Workshop,2007:1-8.
[2] 李群智,贾阳,彭松,等. 月面巡视探测器任务规划顶层设计与实现[J]. 深空探测学报(中英文),2017,4(1):58-65
LI Q Z,JIA Y,PENG S,et al. Top design and implementation of the lunar rover mission planning[J]. Journal of Deep Space Exploration,2017,4(1):58-65
[3] PéREZ-AYúCAR M,ASHMAN M,ALMEIDA M,et al. The Rosetta science operations and planning implementation[J]. Acta Astronautica,2018,152:163-174
[4] 陈德相,徐瑞,崔平远. 航天器资源约束的时间拓扑排序处理方法[J]. 宇航学报,2014,35(6):669-676
CHEN D X,XU R,CUI P Y. A temporal topological sort processing method for spacecraft resources constraints[J]. Journal of Astronautics,2014,35(6):669-676
[5] 金颢,徐瑞,崔平远,等. 基于状态转移图的启发式深空探测器任务规划方法[J]. 深空探测学报(中英文),2019,6(4):364-368
JIN H,XU R,CUI P Y,et al. Heuristic search based on state transition graphs for deep space task planning[J]. Journal of Deep Space Exploration,2019,6(4):364-368
[6] BRESINA J,DEARDEN R,MEULEAU N,et al. Planning under continuous time and resource uncertainty:a challenge for AI[C]//Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence. San Francisco,CA:Morgan Kaufmann Publishers Inc.,2002:77-84.
[7] 徐瑞,陈超,崔平远,等. 航天器自主任务规划修复技术研究进展[J]. 宇航学报,2019,40(7):733-741
XU R,CHEN C,CUI P Y,et al. Research on spacecraft autonomous mission plan repair[J]. Journal of Astronautics,2019,40(7):733-741
[8] NEBEL B,KOEHLER J. Plan reuse versus plan generation:a theoretical and empirical analysis[J]. Artificial Intelligence,1995,76(1):427-454
[9] CHIEN S,KNIGHT R,STECHERT A,et al. Using iterative repair to improve the responsiveness of planning and scheduling[C]//Proceedings of the Fifth International Conference on Artificial Intelligence Planning and Scheduling. Menlo Park,California:The AIAA Press,2000.
[10] CHEN C,XU R,ZHU S Y,et al. RPRS:a reactive plan repair strategy for rapid response to plan failures of deep space missions[J]. Acta Astronautica,2020,175:155-162
[11] FOX M,GEREVINI A,LONG D,et al. Plan Stability:replanning versus plan repair[C]//ICAPs’06:Proceedings of the Sixteenth International Conference on International Conference on Automated Planning and Scheduling. Menlo Park,California:The AAAI Press,2005:212-221.
[12] GEREVINI A,SERINA I. Fast plan adaptation through planning graphs:local and systematic search techniques[C]//Proceedings of the Fifth International Conference on Artificial Intelligence Planning Systems. Breckenridge,CO:[s. n. ],2000.
[13] SCALA E,MICALIZIO R,TORASSO P. ReCon:an online task reconfiguration approach for robust plan execution[C]//The Sixth International Conference on Agents and Artificial Intelligence (ICAART). ESEO,Angers,France:[s. n. ],2014.
[14] GALLIEN M,INGRAND F,LEMAI S. Robot actions planning and execution control for autonomous exploration rovers[C]//International Workshop on Planning under Uncertainty for Autonomous Systems. Monterey,California:[s. n.]:2005.
[15] GUZMAN C,CASTEJON P,ONAINDIA E,et al. Reactive execution for solving plan failures in planning control applications[J]. Integrated Computer Aided Engineering,2015,22(4):343-360
[16] GHALLAB M,NAU D,TRAVERSO P. Automated planning:theory and practice[M]. Amsterdam,Boston:Elsevier/Morgan Kaufmann,2004.
[17] FOX M,LONG D. PDDL2.1:an extension to PDDL for expressing temporal planning domains[J]. Journal of Artificial Intelligence Research,2003,20(20):61-124
[18] DO M B,KAMBHAMPATI S. Sapa:a multi-objective metric temporal planner[J]. Journal of Artificial Intelligence Research,2003,20(20):155-194
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