室内导航系统的语义导向使用大型语言模型.
Marzena Halama1, Sławomir Nowak2, Konrad Połys2
1Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100, Gliwice, Poland. mhalama@iitis.pl.
Scientific reports
|November 25, 2025
概括
本研究介绍了一种使用大型语言模型 (LLM) 来增强自主机器人导航的AI驱动系统. 它改善了物体识别和室内环境中人机交互.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 自主机器人对于室内导航至关重要,但缺乏无的人类互动和环境语义理解.
- 目前的系统在解释复杂的室内空间和有效地与人类合作方面面临限制.
研究的目的:
- 开发一个人工智能驱动的物体识别系统,用于自主室内导航.
- 通过使用大型语言模型 (LLM) 增强人机协作和对环境的语义理解.
主要方法:
- 基于视觉的映射与自然语言处理 (NLP) 和交互能力的整合.
- 利用多式输入和矢量空间分析来增强感知.
- 使用先进的LLM,如GPT-4视觉和双子座,以实现上下文意识的响应.
主要成果:
- 在室内环境中实现了增强的对象识别和语义嵌入.
- 实现了人类和自主机器人之间更直观的协作,用于导航任务.
- 证明了对环境有意识的反应,以改善空间理解.
结论:
- 由LLM增强的拟议的AI驱动系统为自主室内导航提供了一个新的框架.
- 这种方法显著改善了复杂环境中的空间理解和动态交互.
- 在导航中为无的人机器人协作设定了新的标准.
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