QuadAgent：一种基于视觉-语言引导的四旋翼敏捷飞行响应代理系统
QuadAgent: A Responsive Agent System for Vision-Language Guided Quadrotor Agile Flight
AI总结：
QuadAgent通过异步多代理架构解耦高层推理与低层控制，利用Impression Graph维护场景记忆并结合视觉避障网络确保飞行安全，实现在复杂环境中高速导航与响应。
摘要：
我们提出QuadAgent，一种无需训练的代理系统，用于由视觉-语言输入引导的敏捷四旋翼飞行。不同于以往端到端或串行代理方法，QuadAgent采用异步多代理架构解耦高层推理与低层控制：前景工作流代理处理主动任务和用户指令，背景代理执行前瞻推理。系统通过Impression Graph维护场景记忆，该图是一种轻量级拓扑图，由稀疏关键帧构建而成，并通过基于视觉的障碍物避障网络确保安全飞行。仿真结果表明，QuadAgent在效率和响应性方面优于基线方法。实际实验显示，它能够解读复杂指令、对周围环境进行推理，并在速度高达5 m/s的情况下导航杂乱的室内空间。
英文摘要：
We present QuadAgent, a training-free agent system for agile quadrotor flight guided by vision-language inputs. Unlike prior end-to-end or serial agent approaches, QuadAgent decouples high-level reasoning from low-level control using an asynchronous multi-agent architecture: Foreground Workflow Agents handle active tasks and user commands, while Background Agents perform look-ahead reasoning. The system maintains scene memory via the Impression Graph, a lightweight topological map built from sparse keyframes, and ensures safe flight with a vision-based obstacle avoidance network. Simulation results show that QuadAgent outperforms baseline methods in efficiency and responsiveness. Real-world experiments demonstrate that it can interpret complex instructions, reason about its surroundings, and navigate cluttered indoor spaces at speeds up to 5 m/s.
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。
