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Heuristic search via graphical structure in temporal interval-based planning for deep space exploration

机译:通过基于时间间隔的基于时间间隔的图形结构进行启发式搜索,用于深空探索

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摘要

Operations of conventional spacecraft used to be planned on ground and are uploaded as telecommands and executed on board at due time. However, because of difficulties in communicating with distant spacecraft, direct human control for the spacecraft is infeasible. Therefore, great hopes are placed on automated planning techniques to enhance the security of the spacecraft. By deciding a coon, as to support opportunistic science, the planner is mplex set of activities or states, an onboard planner is able to effectively arrange the daily tasks on a spacecraft. In additialso required to respond in the shortest possible time. Typically, to better characterize the spacecraft, the timeline-based knowledge representation benefits from its powerful ability to describe time and temporal behaviors, which is essential to effectively address real world problems. Compliant with the representation method, an elegant approach is devised for search guidance and solving problems efficiently in space-like contexts. Specifically, the key technique we build on is the heuristic estimate strategy based on a graphical structure defined in the model. Furthermore, a search algorithm joint with the heuristic function is proposed to avoid redundant work. By evaluating the branching nodes, this approach is able to prune irrelevant search space and make improvements in onboard planning efficiency. Our experiments exhibit an excellent performance on tested instances compared to Europa2.
机译:常规航天器的操作用于在地面上进行计划,并在电信中上传并在船上执行。然而,由于困难与遥远的航天器沟通,航天器的直接人体控制是不可行的。因此,良好的希望被置于自动化规划技术上,以提高航天器的安全性。通过决定浣熊,为了支持机会理解科学,计划者是混水的活动或国家,一个船上的计划者能够有效地安排航天器上的每日任务。在Additialso中需要在最短的时间内响应。通常,为了更好地表征航天器,基于时间轴的知识表示从其强大的描述时间和时间行为的能力效益,这对于有效地解决现实世界问题至关重要。符合表示方法,设计了优雅的方法,用于在空间背景下有效地搜索引导和解决问题。具体而言,我们构建的关键技术是基于模型中定义的图形结构的启发式估算策略。此外,提出了一种具有启发式功能的搜索算法关节,以避免冗余工作。通过评估分支节点,这种方法能够修剪无关的搜索空间,并改进车载规划效率。与EuroPA2相比,我们的实验表现出测试实例的出色性能。

著录项

  • 来源
    《Acta astronautica》 |2020年第1期|400-412|共13页
  • 作者单位

    Beijing Inst Technol Inst Deep Space Explorat Technol Beijing 100081 Peoples R China|Minist Ind & Informat Technol Key Lab Autonomous Nav & Control Deep Space Explo Beijing 100081 Peoples R China;

    Beijing Inst Technol Inst Deep Space Explorat Technol Beijing 100081 Peoples R China|Minist Ind & Informat Technol Key Lab Autonomous Nav & Control Deep Space Explo Beijing 100081 Peoples R China;

    Beijing Inst Technol Inst Deep Space Explorat Technol Beijing 100081 Peoples R China|Minist Ind & Informat Technol Key Lab Autonomous Nav & Control Deep Space Explo Beijing 100081 Peoples R China;

    Beijing Inst Technol Inst Deep Space Explorat Technol Beijing 100081 Peoples R China|Minist Ind & Informat Technol Key Lab Autonomous Nav & Control Deep Space Explo Beijing 100081 Peoples R China;

    Beijing Inst Technol Inst Deep Space Explorat Technol Beijing 100081 Peoples R China|Minzu Univ China Sch Informat Engn Beijing 100081 Peoples R China;

    DFH Satellite Co LTD Beijing 100094 Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Planning; Heuristic search; Deep space exploration;

    机译:规划;启发式搜索;深空探索;

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