智能导览机器人可自主导航并实时对话，提升博物馆参观体验。
Next-Gen Museum Guides: Autonomous Navigation and Visitor Interaction with an Agentic Robot
- 用大语言模型实现基于上下文的实时问答交互。
- 通过SLAM技术实现自动导航与路线动态调整。
- 34人实测显示体验良好，但理解与响应仍有不足。
自主机器人正被引入公共空间以提升用户体验，尤其在文化与教育场景中。本文介绍了自主博物馆导览机器人Alter-Ego的设计、实现与评估，该机器人融合先进导航与交互能力。其采用前沿大型语言模型（LLMs）实现实时、上下文感知的问答互动，使游客可就展品进行对话交流；同时运用稳健的同步定位与地图构建（SLAM）技术，实现博物馆空间内的无缝导航及基于用户请求的路径自适应。系统在真实博物馆环境中对34名参与者进行了测试，结合访客-机器人对话的定性分析与互动前后问卷的定量分析。结果表明，机器人整体获得积极评价，显著增强了参观体验，尽管在理解力与响应速度方面仍存在局限。本研究揭示了文化空间中人机交互的潜力，强调了人工智能驱动机器人在促进可访问性与知识获取方面的前景，同时也指出了其在复杂现实环境部署中的当前挑战。
原文摘要 · Abstract (English)
Autonomous robots are increasingly being tested into public spaces to enhance user experiences, particularly in cultural and educational settings. This paper presents the design, implementation, and evaluation of the autonomous museum guide robot Alter-Ego equipped with advanced navigation and interactive capabilities. The robot leverages state-of-the-art Large Language Models (LLMs) to provide real-time, context aware question-and-answer (Q&A) interactions, allowing visitors to engage in conversations about exhibits. It also employs robust simultaneous localization and mapping (SLAM) techniques, enabling seamless navigation through museum spaces and route adaptation based on user requests. The system was tested in a real museum environment with 34 participants, combining qualitative analysis of visitor-robot conversations and quantitative analysis of pre and post interaction surveys. Results showed that the robot was generally well-received and contributed to an engaging museum experience, despite some limitations in comprehension and responsiveness. This study sheds light on HRI in cultural spaces, highlighting not only the potential of AI-driven robotics to support accessibility and knowledge acquisition, but also the current limitations and challenges of deploying such technologies in complex, real-world environments.
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