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.
@article{arxiv.2602.10910,
title = {Safe mobility support system using crowd mapping and avoidance route planning using VLM},
author = {Sena Saito and Kenta Tabata and Renato Miyagusuku and Koichi Ozaki},
journal= {arXiv preprint arXiv:2602.10910},
year = {2026}
}