在极其资源有限的平台上惯性导航:方法,机遇和挑战
Swapnil Sayan Saha1, Yayun Du2, Sandeep Singh Sandha3
1University of California, Los Angeles, Los Angeles, CA, USA.
概括
本研究介绍了AI增强的惯性导航,用于资源有限的物联网 (IoT) 平台. 它引入了轻量级的,基于物理的模型,用于在没有GPS的环境中准确地定位,克服了传统方法的局限性.
科学领域:
- 机器人技术和嵌入式系统
- 人工智能的人工智能
- 传感器融合式传感器
背景情况:
- 惯性导航为GPS拒绝地区的物联网 (IoT) 设备提供低成本的本地化.
- 传统的方法与环境复杂性和漂移作斗争.
- 基于人工智能的方法看起来很有前途,但资源密集,缺乏物理保证.
研究的目的:
- 探索在物联网平台上部署实时AI增强惯性导航的方法.
- 开发用于惯性测距的轻量级,高效和准确的AI模型.
- 为解决AI驱动导航领域适应和数据效率方面的挑战.
主要方法:
- 平台意识的神经架构寻找超轻的深度神经惯性测距模型.
- 神经符号AI技术用于创建基于物理的可解释导航模型.
- 有效的数据收集和标签策略,用于特定领域的模型微调.
主要成果:
- 开发了神经惯性测距模型,比最先进的AI方法小31-134×,具有可比或更高的本地化分辨率.
- 生成适用于各种平台的模型,包括人类,动物,水下传感器,飞行器和机器人.
- 使用最小标记数据 (1分钟) 证明有效的域名适应.
结论:
- 人工智能增强的惯性导航可以在资源有限的物联网平台上有效地部署.
- 轻量级,神经符号和域适应模型为强大的本地化提供了可行的途径.
- 需要进一步的研究,以解决现实世界人工智能驱动的导航系统的公开挑战.
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