Vision And Language Navigation For Uavs Progress C

---
name: vision-and-language-navigation-for-uavs-progress-c
description: 'Research paper: Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Ro'
metadata:
  source: arXiv
  arxiv_id: 2604.13654
  published: 2026-04-15
  utility_score: 1.0
  keywords: multi-agent, agentic, long-horizon, reasoning, benchmark, evaluation, coordination
---

# Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Roadmap

**arXiv ID:** 2604.13654  
**Published:** 2026-04-15  
**Utility Score:** 1.0  
**URL:** http://arxiv.org/abs/2604.13654

## Authors
Hanxuan Chen, Jie Zheng, Siqi Yang

## Categories
cs.RO

## Abstract
Vision-and-Language Navigation for Unmanned Aerial Vehicles (UAV-VLN) represents a pivotal challenge in embodied artificial intelligence, focused on enabling UAVs to interpret high-level human commands and execute long-horizon tasks in complex 3D environments. This paper provides a comprehensive and structured survey of the field, from its formal task definition to the current state of the art. We establish a methodological taxonomy that charts the technological evolution from early modular and deep learning approaches to contemporary agentic systems driven by large foundation models, including Vision-Language Models (VLMs), Vision-Language-Action (VLA) models, and the emerging integration of generative world models with VLA architectures for physically-grounded reasoning. The survey systematically reviews the ecosystem of essential resources simulators, datasets, and evaluation metrics that facilitates standardized research. Furthermore, we conduct a critical analysis of the primary challenges impeding real-world deployment: the simulation-to-reality gap, robust perception in dynamic outdoor settings, reasoning with linguistic ambiguity, and the efficient deployment of large models on resource-constrained hardware. By synthesizing current benchmarks and limitations, this survey concludes by proposing a forward-looking research roadmap to guide future inquiry into key frontiers such as multi-agent swarm coordination and air-ground collaborative robotics.

## Matched Keywords
multi-agent, agentic, long-horizon, reasoning, benchmark, evaluation, coordination

## Relevance to AI Agents
This paper is highly relevant to AI agent systems research with focus on:
- multi-agent, agentic, long-horizon, reasoning, benchmark

## Quick Reference
```bash
# View paper
open http://arxiv.org/abs/2604.13654

# Download PDF
open http://arxiv.org/pdf/2604.13654.pdf
```

---
*Auto-generated from arXiv on 2026-04-17*

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