FALCON-S：固定翼地效空气动力学模拟器与飞行控制学习套件
FALCON-S: Fixed-wing ground-effect Aerodynamics Simulator and Flight Control Learning Suite
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中文总结 AI 辅助
提出FALCON-S，一个模块化高保真仿真框架，建模完整六自由度刚体物理和地效空气动力学，支持CPU/GPU并行，用于近地固定翼飞行控制策略的开发与基准测试。
中文摘要 AI 辅助
我们提出一个模块化、高保真的仿真框架，用于开发和基准测试近地飞行的固定翼空中机器人的飞行控制策略。与依赖简化或悬停导向动力学的现有模拟器不同，我们的框架建模了完整的六自由度刚体物理、半经验地效空气动力学、执行器动力学、传感器噪声和环境扰动。这种物理真实性结合模块化组件设计，使得在现实条件下对低空飞行行为进行系统分析成为可能。该模拟器通过Torch和NVIDIA Warp支持CPU和GPU后端，实现高吞吐量并行执行，适用于大规模强化学习训练和最优控制滚动。一个统一接口容纳了多种控制器（包括强化学习和光学控制算法），用于高度调节和轨迹跟踪等任务。还支持与X-Plane和JSBSim的交叉验证，以促进工程集成和视觉保真度。
英文摘要
We present a modular, high-fidelity simulation framework for the development and benchmarking of flight control strategies in fixed-wing aerial robots operating near the ground. Unlike existing simulators that rely on simplified or hover-oriented dynamics, our framework models full 6DoF rigid-body physics, semi-empirical ground-effect aerodynamics, actuator dynamics, sensor noise, and environmental disturbances. This physical realism, combined with modular component design, enables systematic analysis of low-altitude flight behavior under realistic conditions. The simulator supports both CPU and GPU backends via Torch and NVIDIA Warp, enabling high-throughput parallel execution suitable for large-scale reinforcement learning training and optimal control rollouts. A unified interface accommodates a range of controllers (both RL and optical control algorithms) across tasks such as altitude regulation and trajectory tracking. Cross-validation with X-Plane and JSBSim is also supported to facilitate engineering integration and visual fidelity.
发表机构
- Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg（卢森堡大学安全、可靠性与信任跨学科中心（SnT））
- Earth Speacies Project（地球物种项目）
机构由 AI 辅助整理，请以论文原文为准。
