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Effective search strategy via internal state transition graphs on onboard planning for deep space probes

机译：通过机载计划中的内部状态转换图进行有效的搜索策略，用于深空探测器

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摘要

As to support the mission of Mars exploration in China, automated onboard planning is required to enhance the security and robustness of deep space probes. Onboard planning here is a term that defines a complex set of activities or states aiming at deciding the daily tasks on a probe and at figuring out if mission goals are met. Deep space onboard planning requires modeling of complex operation constraints and focusing on intricate state transitions of involved subsystems. Also, devices of various operation modes and multiple functionalities, which are ubiquitous in physical systems, are intractable in onboard planning and have not been effectively handled. To cope with these difficulties, we introduce an approach of knowledge representation that explicitly establishes the mentioned features. The key techniques we build on are the notion of timeline-based planning tasks and heuristic estimate method designed on internal state transition graphs. Furthermore, state transitions have provided crucial information for search guidance, and a search algorithm joint with internal state transition graph heuristic method is proposed to avoid redundant work. Finally, we run comprehensive experiments on selected domains, and our techniques present an excellent performance compared to the algorithm in Europa2.
机译：为了支持火星探测在中国的任务，需要进行自动机载计划，以增强深空探测器的安全性和鲁棒性。机上计划是一个术语，用于定义一组复杂的活动或状态，旨在决定探测器的日常任务并确定任务目标是否达到。太空深空机载计划要求对复杂的操作约束进行建模，并专注于所涉及子系统的复杂状态转换。而且，在物理系统中普遍存在的各种操作模式和多种功能的设备在机载计划中是棘手的，并且没有被有效地处理。为了解决这些困难，我们引入了一种知识表示的方法，该方法明确地建立了上述功能。我们基于的关键技术是基于时间轴的计划任务的概念以及在内部状态转换图上设计的启发式估计方法。此外，状态转换为搜索指导提供了关键信息，并且提出了一种与内部状态转换图启发式方法结合的搜索算法，以避免多余的工作。最后，我们在选定的域上进行了全面的实验，与Europa2中的算法相比，我们的技术具有出色的性能。

著录项

  • 来源
    《Acta astronautica》 |2018年第7期|235-245|共11页
  • 作者

    Xu Rui; Jin Hao; Xu Wenming; Cui Pingyuan; Han Xiaodong;

  • 作者单位

    Beijing Inst Technol, Inst Deep Space Explorat Technol, Beijing 100081, Peoples R China;

    Beijing Inst Technol, Inst Deep Space Explorat Technol, Beijing 100081, Peoples R China;

    Beijing Inst Technol, Inst Deep Space Explorat Technol, Beijing 100081, Peoples R China;

    Beijing Inst Technol, Inst Deep Space Explorat Technol, Beijing 100081, Peoples R China;

    CAST, Inst Telecommun Satellite, 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

    Deep space probes; Timeline-based planning tasks; Internal state transition graphs; Heuristic planning approach;

    机译：深空探测器;基于时间轴的计划任务;内部状态转换图;启发式计划方法;

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