Guide-LLM: An Embodied LLM Agent and Text-Based Topological Map for Robotic Guidance of People with Visual Impairments
- Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
- Publication Type:
- Conference Proceeding
- Citation:
- 2025 18th International Conference on Sensing Technology (ICST), 2025, 00, pp. 1-6
- Issue Date:
- 2025-12-03
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Navigation presents a significant challenge for persons with visual impairments (PVI). While traditional aids such as white canes and guide dogs are invaluable, they fall short in delivering detailed spatial information and precise guidance to desired locations. Recent developments in large language models (LLMs) and vision-language models (VLMs) offer new avenues for enhancing assistive navigation. In this paper, using vision sensing, we introduce Guide-LLM, an embodied LLM-based agent designed to assist PVI in navigating large indoor environments. Our approach features a novel text-based topological map that enables the LLM to plan global paths using a simplified environmental representation, focusing on straight paths and right-angle turns to facilitate navigation. Additionally, we utilize the LLM’s commonsense reasoning for hazard detection and personalized path planning based on user preferences. Simulated experiments demonstrate the system’s efficacy in navigating indoor environments based on user queries, underscoring its potential as a significant advancement in assistive technology. The results highlight Guide-LLM’s ability to offer efficient, adaptive, and personalized navigation assistance, pointing to promising advancements in this field. Code and replication details: https://github.com/awd1779/Guide-LLM
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