VLN-Pilot: 大型视觉-语言模型作为自主室内无人机操作员
VLN-Pilot: Large Vision-Language Model as an Autonomous Indoor Drone Operator
AI总结：
VLN-Pilot利用大型视觉-语言模型实现自主室内无人机导航，通过自然语言指令和视觉感知结合，提高飞行任务的自主性和安全性。
摘要：
本文介绍了VLN-Pilot，一种新颖的框架，其中大型视觉-语言模型（VLLM）担任室内无人机导航的人类飞行员角色。通过利用VLLM的多模态推理能力，VLN-Pilot解读自由形式的自然语言指令，并将其基于视觉观察进行规划和执行无人机轨迹，在GPS拒止的室内环境中。与传统的基于规则或几何路径规划方法不同，我们的框架整合了语言驱动的语义理解与视觉感知，使能够具备情境感知的高级飞行行为，且具有最小的任务特定工程。VLN-Pilot通过推理空间关系、障碍物规避和动态响应于意外事件，支持无人机的完全自主指令跟随。我们在定制的逼真室内模拟基准上验证了该框架，并展示了由VLLM驱动的代理在复杂指令跟随任务中的能力，包括具有多个语义目标的长周期导航。实验结果突显了用语言引导的自主代理取代远程无人机飞行员的潜力，为在诸如检查、搜索与救援和设施监控等任务中实现可扩展、人友好的室内无人机控制开辟了途径。我们的结果表明，基于VLLM的飞行员可能大幅减少操作员的工作负担，同时提高安全性和任务灵活性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
