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

• URL: http://arxiv.org/abs/2501.05014v1
• Date: Thu, 09 Jan 2025 07:15:59 GMT
• Title: UAV-VLA: Vision-Language-Action System for Large Scale Aerial Mission Generation
• Authors: Oleg Sautenkov, Yasheerah Yaqoot, Artem Lykov, Muhammad Ahsan Mustafa, Grik Tadevosyan, Aibek Akhmetkazy, Miguel Altamirano Cabrera, Mikhail Martynov, Sausar Karaf, Dzmitry Tsetserukou,
• Abstract summary: 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.
• Score: 1.8742629471785477
• License:
• 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.

Related papers

• Aerial Vision-and-Language Navigation via Semantic-Topo-Metric Representation Guided LLM Reasoning [48.33405770713208]
  We propose an end-to-end framework for aerial VLN tasks, where the large language model (LLM) is introduced as our agent for action prediction. We develop a novel Semantic-Topo-Metric Representation (STMR) to enhance the spatial reasoning ability of LLMs. Experiments conducted in real and simulation environments have successfully proved the effectiveness and robustness of our method.
  arXiv  Detail & Related papers  (2024-10-11T03:54:48Z)
• Towards Realistic UAV Vision-Language Navigation: Platform, Benchmark, and Methodology [38.2096731046639]
  Recent efforts in UAV vision-language navigation predominantly adopt ground-based VLN settings. We propose solutions from three perspectives: platform, benchmark, and methodology.
  arXiv  Detail & Related papers  (2024-10-09T17:29:01Z)
• Affordances-Oriented Planning using Foundation Models for Continuous Vision-Language Navigation [64.84996994779443]
  We propose a novel Affordances-Oriented Planner for continuous vision-language navigation (VLN) task. Our AO-Planner integrates various foundation models to achieve affordances-oriented low-level motion planning and high-level decision-making. Experiments on the challenging R2R-CE and RxR-CE datasets show that AO-Planner achieves state-of-the-art zero-shot performance.
  arXiv  Detail & Related papers  (2024-07-08T12:52:46Z)
• LangNav: Language as a Perceptual Representation for Navigation [63.90602960822604]
  We explore the use of language as a perceptual representation for vision-and-language navigation (VLN) Our approach uses off-the-shelf vision systems for image captioning and object detection to convert an agent's egocentric panoramic view at each time step into natural language descriptions.
  arXiv  Detail & Related papers  (2023-10-11T20:52:30Z)
• BEVBert: Multimodal Map Pre-training for Language-guided Navigation [75.23388288113817]
  We propose a new map-based pre-training paradigm that is spatial-aware for use in vision-and-language navigation (VLN) We build a local metric map to explicitly aggregate incomplete observations and remove duplicates, while modeling navigation dependency in a global topological map. Based on the hybrid map, we devise a pre-training framework to learn a multimodal map representation, which enhances spatial-aware cross-modal reasoning thereby facilitating the language-guided navigation goal.
  arXiv  Detail & Related papers  (2022-12-08T16:27:54Z)
• Think Global, Act Local: Dual-scale Graph Transformer for Vision-and-Language Navigation [87.03299519917019]
  We propose a dual-scale graph transformer (DUET) for joint long-term action planning and fine-grained cross-modal understanding. We build a topological map on-the-fly to enable efficient exploration in global action space. The proposed approach, DUET, significantly outperforms state-of-the-art methods on goal-oriented vision-and-language navigation benchmarks.
  arXiv  Detail & Related papers  (2022-02-23T19:06:53Z)
• Vision-Based UAV Self-Positioning in Low-Altitude Urban Environments [20.69412701553767]
  Unmanned Aerial Vehicles (UAVs) rely on satellite systems for stable positioning. In such situations, vision-based techniques can serve as an alternative, ensuring the self-positioning capability of UAVs. This paper presents a new dataset, DenseUAV, which is the first publicly available dataset designed for the UAV self-positioning task.
  arXiv  Detail & Related papers  (2022-01-23T07:18:55Z)
• A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments [68.31522961125589]
  We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles. The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map. Results indicate that the proposed multi-UAV system has improvements in terms of time-cost, the proportion of search area surveyed, as well as successful rates for search and rescue missions.
  arXiv  Detail & Related papers  (2021-07-19T12:54:04Z)
• Towards Deep Learning Assisted Autonomous UAVs for Manipulation Tasks in GPS-Denied Environments [10.02675366919811]
  This paper is primarily focused on the task of assembling large 3D structures in outdoors and GPS-denied environments. Our framework is deployed on the specified UAV in order to report the performance analysis of the individual modules.
  arXiv  Detail & Related papers  (2021-01-16T09:20:46Z)
• UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning [16.720630804675213]
  This work presents a method for autonomous UAV path planning based on deep reinforcement learning (DRL) We compare coverage path planning ( CPP), where the UAV's goal is to survey an area of interest to data harvesting (DH), where the UAV collects data from distributed Internet of Things (IoT) sensor devices. By exploiting structured map information of the environment, we train double deep Q-networks (DDQNs) with identical architectures on both distinctly different mission scenarios.
  arXiv  Detail & Related papers  (2020-10-14T09:59:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.