基于知识图谱的深空探测器任务规划建模

Translated title of the contribution: Modeling of Mission Planning for Deep Space Probe Based on Knowledge Graph

Xin Wang, Qingjie Zhao*, Rui Xu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Designing a deep-space flexible multi-agent probe capable of autonomous task planning is an important direction for future research and development of deep space exploration technology. Multi-agent deep-space probes involve multiple objects, complex constraints and the uncertain deep space environment in mission planning, but traditional mission planning languages may not describe mission planning accurately, intuitively and concisely. In this paper, a knowledge graph is proposed to represent the planning knowledge for a multi-agent deep space probe. The method first carries out knowledge extraction from the deep space probe, then associates the probe with its state and actions by knowledge fusion, and finally mines the potential relationships between agents by knowledge processing. Compared with MA-PDDL, the method proposed in this paper is simpler and more intuitive, which enables the probe to describe its mission planning autonomously, flexibly, and accurately.

Translated title of the contributionModeling of Mission Planning for Deep Space Probe Based on Knowledge Graph
Original languageChinese (Traditional)
Pages (from-to)315-323
Number of pages9
JournalJournal of Deep Space Exploration
Volume8
Issue number3
DOIs
Publication statusPublished - Jun 2020

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