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Translated title of the contribution: Spacecraft game decision making for threat avoidance of space targets based on machine learning

Honglin Zhang, Jianjun Luo, Weihua Ma

Research output: Contribution to journalArticlepeer-review

Abstract

An intelligent decision-making framework and a deep reinforcement learning-based autonomous decision-making method are proposed for the spacecraft decision-making in avoiding the threat of space targets. Taking into ac⁃ count the maneuvering characteristics of space targets and the gameplay of threat avoidance,an intelligent game decision-making framework for spacecraft threat avoidance is proposed based on the Observation-Orientation-Decision-Action(OODA)loop decision-making idea and machine learning techniques. Based on this framework and inference on the motion intentions of space targets,a deep reinforcement learning-based spacecraft maneuver decision-making algorithm and training environment are designed to enable spacecraft decision-making control with game response ca⁃ pability,which realizes the avoidance response to the typical motion intentions of space targets. Furthermore,the generalization of spacecraft autonomous maneuvering decision-making algorithm and its adaptability to possible uncer⁃ tain maneuvers of space targets are improved by using the self-play learning technique. Finally,the effectiveness of our proposed method is verified through simulations.

Translated title of the contributionSpacecraft game decision making for threat avoidance of space targets based on machine learning
Original languageChinese (Traditional)
Article number329136
JournalHangkong Xuebao/Acta Aeronautica et Astronautica Sinica
Volume45
Issue number8
DOIs
StatePublished - 25 Apr 2024

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