VLN-Pilot: 大規模視覚言語モデルを屋内ドローンの自律操縦者として活用
VLN-Pilot: Large Vision-Language Model as an Autonomous Indoor Drone Operator
大規模視覚言語モデル(VLLM)をドローンのパイロット役に据え、自然言語指示と視覚情報から屋内での自律飛行を実現するフレームワークを提案し、フォトリアルなシミュレーションで有効性を示した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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著者: Bessie Dominguez-Dager, Sergio Suescun-Ferrandiz, Felix Escalona, Francisco Gomez-Donoso, Miguel Cazorla
分類: cs.RO, cs.CV
原文アブストラクト
This paper introduces VLN-Pilot, a novel framework in which a large Vision-and-Language Model (VLLM) assumes the role of a human pilot for indoor drone navigation. By leveraging the multimodal reasoning abilities of VLLMs, VLN-Pilot interprets free-form natural language instructions and grounds them in visual observations to plan and execute drone trajectories in GPS-denied indoor environments. Unlike traditional rule-based or geometric path-planning approaches, our framework integrates language-driven semantic understanding with visual perception, enabling context-aware, high-level flight behaviors with minimal task-specific engineering. VLN-Pilot supports fully autonomous instruction-following for drones by reasoning about spatial relationships, obstacle avoidance, and dynamic reactivity to unforeseen events. We validate our framework on a custom photorealistic indoor simulation benchmark and demonstrate the ability of the VLLM-driven agent to achieve high success rates on complex instruction-following tasks, including long-horizon navigation with multiple semantic targets. Experimental results highlight the promise of replacing remote drone pilots with a language-guided autonomous agent, opening avenues for scalable, human-friendly control of indoor UAVs in tasks such as inspection, search-and-rescue, and facility monitoring. Our results suggest that VLLM-based pilots may dramatically reduce operator workload while improving safety and mission flexibility in constrained indoor environments.
