ESARBench is the first unified benchmark for MLLM-driven UAV agents that must explore, locate clues, and decide on victim positions in photorealistic simulated SAR environments.
Uavs meet agentic ai: A multidomain survey of autonomous aerial intelligence and agentic uavs,
4 Pith papers cite this work. Polarity classification is still indexing.
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A survey of UAV vision-and-language navigation that establishes a methodological taxonomy, reviews resources and challenges, and proposes a forward-looking research roadmap.
A universal LLM-to-drone interface is implemented via the Model Context Protocol (MCP) and Mavlink, demonstrated with real UAV flight control and simulated flights using live map data.
A comprehensive survey on low-altitude wireless network (LAWN) systems covering fundamentals, evolution of designs, performance metrics, privacy and security concerns, and airspace structuring for practical deployment.
citing papers explorer
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ESARBench: A Benchmark for Agentic UAV Embodied Search and Rescue
ESARBench is the first unified benchmark for MLLM-driven UAV agents that must explore, locate clues, and decide on victim positions in photorealistic simulated SAR environments.
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Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Roadmap
A survey of UAV vision-and-language navigation that establishes a methodological taxonomy, reviews resources and challenges, and proposes a forward-looking research roadmap.
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A Universal Large Language Model -- Drone Command and Control Interface
A universal LLM-to-drone interface is implemented via the Model Context Protocol (MCP) and Mavlink, demonstrated with real UAV flight control and simulated flights using live map data.
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Low-Altitude Wireless Networks: A Comprehensive Survey
A comprehensive survey on low-altitude wireless network (LAWN) systems covering fundamentals, evolution of designs, performance metrics, privacy and security concerns, and airspace structuring for practical deployment.