Autonomous mobile robots offer promising solutions for labor shortages and increased operational efficiency. However, navigating safely and effectively in dynamic environments, particularly crowded areas, remains challenging. This paper proposes a novel framework that integrates Vision-Language Models (VLM) and Gaussian Process Regression (GPR) to generate dynamic crowd-density maps (``Abstraction Maps'') for autonomous robot navigation. Our approach utilizes VLM's capability to recognize abstract environmental concepts, such as crowd densities, and represents them probabilistically via GPR. Experimental results from real-world trials on a university campus demonstrated that robots successfully generated routes avoiding both static obstacles and dynamic crowds, enhancing navigation safety and adaptability.

翻译：自主移动机器人为劳动力短缺和运营效率提升提供了前景广阔的解决方案。然而，在动态环境，尤其是拥挤区域中实现安全有效的导航仍具挑战性。本文提出一种新颖框架，该框架集成视觉语言模型与高斯过程回归，以生成用于自主机器人导航的动态人群密度地图。我们的方法利用VLM识别抽象环境概念的能力，并通过GPR对其进行概率化表征。在大学校园进行的实地试验结果表明，机器人成功生成了能同时规避静态障碍物与动态人群的路径，从而提升了导航安全性与适应性。