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.
Code (1)
Tasks
Decision MakingLanguage ModelingLanguage ModellingVision-Language-ActionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A tutorial note on collecting simulated data for vision-language-action models
Traditional robotic systems typically decompose intelligence into independent modules for computer vision, natural language processing, and motion control. Vision-Language-Action (VLA) models fundamentally transform this…
PointACT: Vision-Language-Action Models with Multi-Scale Point-Action Interaction
Vision-Language-Action (VLA) models have shown strong potential for general-purpose robotic manipulation by leveraging large pretrained vision-language backbones. However, most existing VLAs rely primarily on 2D visual r…
VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action Model
Vision-Language-Action (VLA) models typically bridge the gap between perceptual and action spaces by pre-training a large-scale Vision-Language Model (VLM) on robotic data. While this approach greatly enhances performanc…
CoVLA: Comprehensive Vision-Language-Action Dataset for Autonomous Driving
Autonomous driving, particularly navigating complex and unanticipated scenarios, demands sophisticated reasoning and planning capabilities. While Multi-modal Large Language Models (MLLMs) offer a promising avenue for thi…
Autonomous DrivingCaption GenerationVision-Language-Actiondroidlet: modular, heterogenous, multi-modal agents
In recent years, there have been significant advances in building end-to-end Machine Learning (ML) systems that learn at scale. But most of these systems are: (a) isolated (perception, speech, or language only); (b) trai…