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通过多模式LLM集成视觉和嗅觉,用于机器人气味源定位

Sunzid Hassan¹, Lingxiao Wang², Khan Raqib Mahmud¹

¹Department of Computer Science, Louisiana Tech University, 201 Mayfield Ave, Ruston, LA 71272, USA.

Sensors (Basel, Switzerland)

|January 8, 2025

概括

本研究介绍了一种用于移动机器人的新型气味源定位 (OSL) 算法,使用大型语言模型 (LLM) 集成视觉和嗅觉. 基于LLM的方法提高了复杂环境中的导航成功和速度,优于传统方法.

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
传感器融合式传感器

背景情况:

气味源定位 (OSL) 对于未知环境中的自主代理来说至关重要.
传统的OSL算法通常依赖于单个感官模式或复杂的监督学习.
环境复杂性,如非单向空气流,可能会破坏仅嗅觉系统.

研究的目的:

为移动机器人开发和验证一种新的OSL算法.
使用大型语言模型 (LLM) 集成视觉和嗅觉传感器模式.
在具有挑战性的现实世界条件下提高OSL性能.

主要方法:

开发了一个基于LLM的OSL算法,包含"高层次推理"和"低层次行动"模块.
集成的多模式传感器数据 (视觉和嗅觉) 进入LLM提示.
在复杂的现实环境中,在移动机器人上实现和测试算法.

主要成果:

拟议的基于LLM的算法与"只有嗅觉","只有视觉"和监督学习融合算法相比,表现出了更高的性能.
在单向和非单向空气流场景中实现了更高的成功率和更短的平均搜索时间.
有效地导航复杂的环境与障碍物和空气流中断.

结论:

基于LLM的多模式传感器融合为气味源定位提供了强大而高效的方法.
在LLMs的零射击推理能力消除了手动知识编码或定制监督模型的需要.
这项技术在实际的搜索和导航任务中显著提升了自主代理的能力.

关键词:

大型语言模型 (LLM)多模式机器人技术多模式气味来源的定位和定位机器人操作系统 (ROS)

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