SWAN generates collision-free drone swarm trajectories from text prompts via video synthesis, adaptive point tracking, planning, and safety filtering, demonstrated in simulation up to 2000 drones and real tests with 49 quadcopters.
Swarm-gpt: Combining large language models with safe motion planning for robot choreography design
3 Pith papers cite this work. Polarity classification is still indexing.
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An LLM agent framework with Web-of-Things standardization and a Model Context Protocol gateway enables natural-language UAV swarm missions, with simulation results showing that explicit grounding tools and guardrails substantially raise execution reliability across six models and four tasks.
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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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.