Beyond Visual Line of Sight: UAVs with Edge AI, Connected LLMs, and VR for Autonomous Aerial Intelligence
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911066779287552 |
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| author | Navarro, Andres de Quinto, Carlos Hernández, José Alberto |
| author_facet | Navarro, Andres de Quinto, Carlos Hernández, José Alberto |
| contents | Unmanned Aerial Vehicles are reshaping Non-Terrestrial Networks by acting as agile, intelligent nodes capable of advanced analytics and instantaneous situational awareness. This article introduces a budget-friendly quadcopter platform that unites 5G communications, edge-based processing, and AI to tackle core challenges in NTN scenarios. Outfitted with a panoramic camera, robust onboard computation, and LLMs, the drone system delivers seamless object recognition, contextual analysis, and immersive operator experiences through virtual reality VR technology. Field evaluations confirm the platform's ability to process visual streams with low latency and sustain robust 5G links. Adding LLMs further streamlines operations by extracting actionable insights and refining collected data for decision support. Demonstrated use cases, including emergency response, infrastructure assessment, and environmental surveillance, underscore the system's adaptability in demanding contexts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15049 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Visual Line of Sight: UAVs with Edge AI, Connected LLMs, and VR for Autonomous Aerial Intelligence Navarro, Andres de Quinto, Carlos Hernández, José Alberto Human-Computer Interaction Unmanned Aerial Vehicles are reshaping Non-Terrestrial Networks by acting as agile, intelligent nodes capable of advanced analytics and instantaneous situational awareness. This article introduces a budget-friendly quadcopter platform that unites 5G communications, edge-based processing, and AI to tackle core challenges in NTN scenarios. Outfitted with a panoramic camera, robust onboard computation, and LLMs, the drone system delivers seamless object recognition, contextual analysis, and immersive operator experiences through virtual reality VR technology. Field evaluations confirm the platform's ability to process visual streams with low latency and sustain robust 5G links. Adding LLMs further streamlines operations by extracting actionable insights and refining collected data for decision support. Demonstrated use cases, including emergency response, infrastructure assessment, and environmental surveillance, underscore the system's adaptability in demanding contexts. |
| title | Beyond Visual Line of Sight: UAVs with Edge AI, Connected LLMs, and VR for Autonomous Aerial Intelligence |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2507.15049 |