Abstract
Unmanned Aerial Vehicles (UAVs) have emerged as a transformative technology across diverse sectors, offering adaptable solutions to complex challenges in both military and civilian domains. Their expanding capabilities present a platform for further advancement by integrating cutting-edge computational tools like Artificial Intelligence (AI) and Machine Learning (ML) algorithms. These advancements have significantly impacted various facets of human life, fostering an era of unparalleled efficiency and convenience. Large Language Models (LLMs), a key component of AI, exhibit remarkable learning and adaptation capabilities within deployed environments, demonstrating an evolving form of intelligence with the potential to approach human-level proficiency. This work explores the significant potential of integrating UAVs and LLMs to propel the development of autonomous systems. We comprehensively review LLM architectures, evaluating their suitability for UAV integration. Additionally, we summarize the state-of-the-art LLM-based UAV architectures and identify novel opportunities for LLM embedding within UAV frameworks. Notably, we focus on leveraging LLMs to refine data analysis and decision-making processes, specifically for enhanced spectral sensing and sharing in UAV applications. Furthermore, we investigate how LLM integration expands the scope of existing UAV applications, enabling autonomous data processing, improved decision-making, and faster response times in emergency scenarios like disaster response and network restoration. Finally, we highlight crucial areas for future research that are critical for facilitating the effective integration of LLMs and UAVs.
Abstract (translated)
无人机（UAVs）作为一种变革性的技术，已经出现在各种领域，为军事和民用领域提供了适应性的解决方案。它们不断扩大的能力为通过整合尖端的计算工具如人工智能（AI）和机器学习（ML）算法，进一步推动进步提供了平台。这些进步对人类生活产生了重大影响，推动了无与伦比的高效和便利的时期。大型语言模型（LLMs），是AI的关键组成部分，在部署环境中表现出惊人的学习和适应能力，表明了一种不断发展的智能形式，具有接近人类水平的能力。 本文探讨了将无人机（UAVs）和LLMs集成以推动自主系统开发的巨大潜力。我们全面回顾了LLM架构，评估其是否适合无人机集成。此外，我们总结了基于LLM的无人机架构的最新进展，并探讨了LLM在无人机框架中嵌入的新机会。值得注意的是，我们重点关注利用LLMs优化数据分析和决策过程，特别是增强无人机应用中的光谱感知和数据共享。 此外，我们研究了LLM集成如何扩大现有无人机应用的范围，实现自主数据处理、改进决策以及在紧急场景如灾难应对和网络恢复中的更快的响应时间。最后，我们强调了未来研究的关键领域，这些领域对于促进LLMs和UAV的有效整合至关重要。
URL
https://arxiv.org/abs/2405.01745
