UAV-VLA: Vision-Language-Action System for Large Scale Aerial Mission Generation

The UAV-VLA (Visual-Language-Action) system is a tool designed to facilitate communication with aerial robots. By integrating satellite imagery processing with the Visual Language Model (VLM) and the powerful capabilities of GPT, UAV-VLA enables users to generate general flight paths-and-action plans through simple text requests. This system leverages the rich contextual information provided by satellite images, allowing for enhanced decision-making and mission planning. The combination of visual analysis by VLM and natural language processing by GPT can provide the user with the path-and-action set, making aerial operations more efficient and accessible. The newly developed method showed the difference in the length of the created trajectory in 22% and the mean error in finding the objects of interest on a map in 34.22 m by Euclidean distance in the K-Nearest Neighbors (KNN) approach. Additionally, the UAV-VLA system generates all flight plans in just 5 minutes and 24 seconds, making it 6.5 times faster than an experienced human operator. The code is available here: https://github.com/sautenich/uav-vla

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Publication Details

Published
2025-03-04
DOI
https://doi.org/10.1109/hri61500.2025.10974117
Citations
20
Primary Topic
Robotics and Sensor-Based Localization
Type
article
Field-Weighted Citation Impact
99.87
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article

UAV-VLA: Vision-Language-Action System for Large Scale Aerial Mission Generation

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20 citations
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article

UAV-VLA: Vision-Language-Action System for Large Scale Aerial Mission Generation

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article en
20 citations

Abstract

The UAV-VLA (Visual-Language-Action) system is a tool designed to facilitate communication with aerial robots. By integrating satellite imagery processing with the Visual Language Model (VLM) and the powerful capabilities of GPT, UAV-VLA enables users to generate general flight paths-and-action plans through simple text requests. This system leverages the rich contextual information provided by satellite images, allowing for enhanced decision-making and mission planning. The combination of visual analysis by VLM and natural language processing by GPT can provide the user with the path-and-action set, making aerial operations more efficient and accessible. The newly developed method showed the difference in the length of the created trajectory in 22% and the mean error in finding the objects of interest on a map in 34.22 m by Euclidean distance in the K-Nearest Neighbors (KNN) approach. Additionally, the UAV-VLA system generates all flight plans in just 5 minutes and 24 seconds, making it 6.5 times faster than an experienced human operator. The code is available here: https://github.com/sautenich/uav-vla

Skolkovo Institute of Science and Technology (RU)
Affordable and clean energy
Openalex Percentile: Top 0%
Robotics and Sensor-Based Localization
99.87
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UAV-VLA: Vision-Language-Action System for Large Scale Aerial Mission Generation — Mikhail Martynov, Oleg Sautenkov, et al. · (2025) | TGRS Research Map | TGRS