用视觉语言模型与高斯回归构建动态人群密度图，让机器人避障更安全。
Safe mobility support system using crowd mapping and avoidance route planning using VLM
- 结合视觉语言模型与高斯回归，将人群密度抽象为概率地图
- 实测在校园环境中成功避开静态障碍和移动人群
- 适合需在复杂人流场景中导航的自主机器人应用
自主移动机器人有望解决劳动力短缺并提升运营效率。然而，在动态环境（尤其是人群密集区域）中实现安全高效导航仍具挑战。本文提出一种新框架，融合视觉语言模型（VLM）与高斯过程回归（GPR），生成动态人群密度图（“抽象地图”），用于自主机器人导航。该方法利用VLM识别如人群密度等抽象环境概念，并通过GPR以概率形式表示。在大学校园的真实场景测试中，机器人成功规划出避开静态障碍物和动态人群的路径，显著提升了导航的安全性与适应性。
原文摘要 · Abstract (English)
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.
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