Towards LLM-powered Assistive Drone for Blind and Low Vision Users

Drones have gained traction as a versatile form of assistive robots for Blind and Low Vision (BLV) people. Nonetheless, novel interaction techniques are required to enable BLV people to communicate with drones naturally. In this work, we built an LLM-powered assistive drone for BLV users. We leverage an LLM to translate high-level user goals to step-by-step instructions for the drone and to extract visual information from the images. Through a formative study with BLV users (N=9), we identified envisioned use cases and desired interaction modalities. Then, we took a participatory and iterative approach to build a prototype, incorporating feedback received from 3 BLV users, as well as 5 domain experts. Finally, we conducted a user study with an additional 6 BLV participants to evaluate the iterated prototype, and received positive feedback. This work is contributing to a growing body of research on harnessing the power of LLMs to build a more inclusive world.

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Publication Details

Published
2026-04-13
DOI
https://doi.org/10.1145/3772318.3791343
Citations
1
Primary Topic
Social Robot Interaction and HRI
Type
article
Field-Weighted Citation Impact
0.00
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article

Towards LLM-powered Assistive Drone for Blind and Low Vision Users

Yize Wei, Hanjun Wu, Bin Adam
1 citations
Social Robot Interaction and HRI
article

Towards LLM-powered Assistive Drone for Blind and Low Vision Users

Yize Wei, Hanjun Wu, Bin Adam
article en
1 citations

Abstract

Drones have gained traction as a versatile form of assistive robots for Blind and Low Vision (BLV) people. Nonetheless, novel interaction techniques are required to enable BLV people to communicate with drones naturally. In this work, we built an LLM-powered assistive drone for BLV users. We leverage an LLM to translate high-level user goals to step-by-step instructions for the drone and to extract visual information from the images. Through a formative study with BLV users (N=9), we identified envisioned use cases and desired interaction modalities. Then, we took a participatory and iterative approach to build a prototype, incorporating feedback received from 3 BLV users, as well as 5 domain experts. Finally, we conducted a user study with an additional 6 BLV participants to evaluate the iterated prototype, and received positive feedback. This work is contributing to a growing body of research on harnessing the power of LLMs to build a more inclusive world.

National University of Singapore (SG), Image and Pervasive Access Laboratory (SG)
Openalex Percentile: Top 35%
Social Robot Interaction and HRI
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