Large Language Models for UAVs: Current State and Pathways to the Future
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 computatio...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
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| Online Access: | https://ieeexplore.ieee.org/document/10643253/ |
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| author | Shumaila Javaid Hamza Fahim Bin He Nasir Saeed |
| author_facet | Shumaila Javaid Hamza Fahim Bin He Nasir Saeed |
| author_sort | Shumaila Javaid |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7bbd3fd870674fe097063ed93e54fb50 |
| institution | Kabale University |
| issn | 2644-1330 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-7bbd3fd870674fe097063ed93e54fb502025-01-30T00:04:37ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302024-01-0151166119210.1109/OJVT.2024.344679910643253Large Language Models for UAVs: Current State and Pathways to the FutureShumaila Javaid0Hamza Fahim1https://orcid.org/0000-0001-6537-7691Bin He2https://orcid.org/0000-0003-3193-6269Nasir Saeed3https://orcid.org/0000-0002-5123-5139Department of Control Science, Engineering, College of Electronics, Information Engineering, Tongji University, Shanghai, ChinaDepartment of Control Science, Engineering, College of Electronics, Information Engineering, Tongji University, Shanghai, ChinaDepartment of Control Science, Engineering, College of Electronics, Information Engineering, Tongji University, Shanghai, ChinaDepartment of Electrical, Communication Engineering, United Arab Emirates University (UAEU), Al Ain, United Arab EmiratesUnmanned 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.https://ieeexplore.ieee.org/document/10643253/UAVslarge language modelsspectral sensingautonomous systemsdecision-making |
| spellingShingle | Shumaila Javaid Hamza Fahim Bin He Nasir Saeed Large Language Models for UAVs: Current State and Pathways to the Future IEEE Open Journal of Vehicular Technology UAVs large language models spectral sensing autonomous systems decision-making |
| title | Large Language Models for UAVs: Current State and Pathways to the Future |
| title_full | Large Language Models for UAVs: Current State and Pathways to the Future |
| title_fullStr | Large Language Models for UAVs: Current State and Pathways to the Future |
| title_full_unstemmed | Large Language Models for UAVs: Current State and Pathways to the Future |
| title_short | Large Language Models for UAVs: Current State and Pathways to the Future |
| title_sort | large language models for uavs current state and pathways to the future |
| topic | UAVs large language models spectral sensing autonomous systems decision-making |
| url | https://ieeexplore.ieee.org/document/10643253/ |
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