Long-Horizon Planning with Large Language Models for Indoor Assistive Navigation of the Visually Impaired

Abstract

For visually impaired individuals, assistive navigation systems play a crucial role in enabling independent mobility. However, long-horizon planning based on natural language (NL) instructions in complex indoor environments remains a significant challenge. Recent studies show the strong potential of Large Language Models (LLMs) in NL understanding and task-level planning. Yet, the inherent limitations of LLMs in mathematical reasoning and their susceptibility to hallucination hinder their reliability in low-level path planning. In this paper, we introduce an LLM-based indoor assistive navigation system that interprets NL instructions from visually impaired users for autonomous navigation. At its core is a novel planning agent that grounds instructions to the environment's topological map and generates optimal route plans. To avoid hallucination in geometric reasoning, the LLM handles only high-level semantic planning, while precise node-level paths are delegated to a classical graph search algorithm. We further implement a wearable assistive device that provides voice and vibrotactile feedback to deliver hands-free navigation. Offline evaluations and real-world experiments demonstrate that our system can reliably plan grounded routes and enable visually impaired users to autonomously complete long-horizon navigation tasks. Anonymous project page is available at https://lhp-ian.github.io.