SkySim: 一种基于ROS2的无人机群自然语言控制仿真环境
SkySim: A ROS2-based Simulation Environment for Natural Language Control of Drone Swarms using Large Language Models
AI总结：
SkySim通过基于ROS2的仿真框架，利用大型语言模型实现无人机群的自然语言控制，结合人工势场算法确保安全执行，验证了空间推理精度和实时碰撞避免能力。
摘要：
无人机群在物流、农业和监视等领域具有广泛的应用，但其控制需要专家知识以确保安全性和可行性。传统静态方法限制了适应性，而大型语言模型（LLMs）能够实现自然语言控制，但因缺乏物理基础而生成不安全的轨迹。本文介绍了SkySim，一种基于ROS2和Gazebo的仿真框架，将LLM的高层规划与底层安全执行解耦。使用Gemini 3.5 Pro，SkySim将用户指令（如“形成圆圈”）转换为空间路径点，基于实时无人机状态进行调整。人工势场（APF）安全过滤器对碰撞避免、运动学限制和地理围栏施加最小调整，确保在20Hz频率下可行执行。对3、10和30个Crazyflie无人机群的实验验证了空间推理的准确性（在测试的几何基元上达到100%）、实时碰撞预防和可扩展性。SkySim使非专家能够迭代优化行为，将AI认知与机器人安全结合，适用于动态环境。未来工作将针对硬件集成。
英文摘要：
Unmanned Aerial Vehicle (UAV) swarms offer versatile applications in logistics, agriculture, and surveillance, yet controlling them requires expert knowledge for safety and feasibility. Traditional static methods limit adaptability, while Large Language Models (LLMs) enable natural language control but generate unsafe trajectories due to lacking physical grounding. This paper introduces SkySim, a ROS2-based simulation framework in Gazebo that decouples LLM high-level planning from low-level safety enforcement. Using Gemini 3.5 Pro, SkySim translates user commands (e.g., "Form a circle") into spatial waypoints, informed by real-time drone states. An Artificial Potential Field (APF) safety filter applies minimal adjustments for collision avoidance, kinematic limits, and geo-fencing, ensuring feasible execution at 20 Hz. Experiments with swarms of 3, 10, and 30 Crazyflie drones validate spatial reasoning accuracy (100% across tested geometric primitives), real-time collision prevention, and scalability. SkySim empowers non-experts to iteratively refine behaviors, bridging AI cognition with robotic safety for dynamic environments. Future work targets hardware integration.
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。
