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在移动机器人部署期间基于语义的情境意识的框架

Tianshu Ruan¹, Aniketh Ramesh¹, Hao Wang¹

¹Extreme Robotics Lab (ERL) and National Center for Nuclear Robotics (NCNR), University of Birmingham, Birmingham, United Kingdom.

Frontiers in robotics and AI

|December 5, 2025

概括

本研究介绍了一个机器人理解危险任务期间复杂环境的框架. 它使用语义指标和情境语义丰富度指标来增强人机组合和情境意识,以便更好地做出决策.

科学领域:

机器人和人工智能机器人和人工智能
人与机器人的交互
应对灾害的技术应对灾害的技术

背景情况:

人机组合 (HRT) 对危险环境至关重要,需要强大的情境意识 (SA).
现有的SA框架往往忽视了对半自主系统至关重要的更高层次的语义理解.
有效的HRT依赖于人类操作员和自主代理人处理复杂的环境信息.

研究的目的:

为移动机器人在远程部署中获取和集成多模式语义SA提出一个可通用的框架.
开发"环境语义指标"和"情境语义丰富性" (SSR) 度量来评估环境复杂性.
在像搜救 (SAR) 这样的关键场景中增强决策和操作员注意力.

主要方法:

为机器人SA.开发了一个框架,将多种语义信息模式结合起来.
引入了用于现场分析的"环境语义指标" (例如风险,人类活动的迹象).
提出了"情境语义丰富性" (SSR) 度量来量化整体环境复杂性.

主要成果:

提出的语义指标有效地捕获了不同场景中的多样化语义信息.
SSR指标准确地反映了整体语义变化,表明了信息丰富的情况.
在模拟灾难环境中对Jackal机器人进行实验验证,证实了框架的敏感性.

结论:

开发的框架增强了在危险环境中移动机器人的语义SA.
SSR指标为评估情况的复杂性和指导操作员干预提供了有价值的工具.
这种方法提高了人机组合在灾难响应等关键应用中的有效性.

关键词:

灾难应对机器人机器人技术人类-机器人合作.搜索和救援机器人机器人技术语义上的理解语义上的理解语义学的语义学情境意识:情况意识.

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