基于元强化学习的小行星防御自主末制导导航算法

IF 4.6 Q1 OPTICS
Yuhao Pu, Chao Bei
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引用次数: 0

摘要

提出了一种基于元强化学习的小行星防御任务鲁棒纯角度制导导航算法。通过近端策略优化训练的递归神经网络用于将机载摄像机实时捕获的视线角度映射到最佳推力。神经网络有效地取代了导航制导系统的作用,同时消除了对动态模型和观测模型的依赖。对该制导导航模型进行了针对小行星Bennu的模拟任务的数值模拟。目标是使航天器能够精确地撞击小行星,尽管存在分散的初始条件,不确定的模型参数,推进器控制误差,姿态控制和测量误差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Angle-only Autonomous Terminal Guidance and Navigation Algorithm for Asteroid Defense based on Meta-reinforcement Learning
Abstract This paper presented a robust angle-only guidance and navigation algorithm for asteroid defense missions based on meta-reinforcement learning. A recurrent neural network, trained via proximal policy optimization, is used to map the line-of-sight angles captured in real-time by the onboard camera to the optimal thrust. The neural network effectively replaces the roles of the navigation and guidance system while simultaneously removing the dependence on dynamic and observation models. The guidance and navigation model is tested on numerical simulations of a simulated mission directed to asteroid Bennu. The objective is to enable the spacecraft to hit the asteroid precisely, despite the presence of scattered initial conditions, uncertain model parameters, thruster control error, and attitude control and measurement error.
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来源期刊
CiteScore
10.70
自引率
0.00%
发文量
27
审稿时长
12 weeks
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