太空飞船神经引导和控制中的最佳性原则
Dario Izzo1, Emmanuel Blazquez1, Robin Ferede2
1Advanced Concepts Team, European Space Research & Technology Centre, Keplerlaan 1, 2200 AG Noordwijk, Netherlands.
Science robotics
|June 19, 2024
概括
神经网络正在学习太空任务的最佳控制,从而实现实时机上决策. 这种方法提高了航天器的自主性和强度,用于行星间转移和着陆.
科学领域:
- 机器人技术和自主系统
- 航空航天工程 航空航天工程
- 人工智能的人工智能
背景情况:
- 太空任务需要有效的船上资源管理才能取得成功.
- 传统方法依赖于基于地面的最佳控制计划.
- 导航和控制的机载计算对于自主性至关重要.
研究的目的:
- 审查用于航天器引导和控制的端到端神经网络.
- 突出神经模型对最佳性原理的学习.
- 探索无人机赛车作为这些AI架构的测试平台.
主要方法:
- 对端到端神经网络的审查,称为指导和控制网络 (G&CNets).
- 对星际转移,行星着陆和近距离作战的G&CNets进行分析.
- 利用无人机比赛作为测试G&CNets的模拟环境.
主要成果:
- 神经网络成功地学习并应用空间飞船控制的最佳性原则.
- G&CNets能够从传感器数据中实时计算最佳动作.
- 无人机赛车环境验证了G&CNets在不确定性下的表现.
结论:
- 端到端的神经网络为增加航天器自主性和稳健性提供了一条道路.
- G&CNets可以适应复杂的动态和不确定性,这对太空探索至关重要.
- 无人机竞赛为推进太空机器人技术中的AI提供了一个有价值的平台.
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