用涡粒子模型实现实时飞行规划，提升固定翼无人机在复杂气流中的机动性能。
Real-Time Planning and Control with a Vortex Particle Model for Fixed-Wing UAVs in Unsteady Flows
- 基于轻量级涡粒子模型，支持GPU加速实时计算
- 在模拟与硬件实验中显著提升后失速机动的稳定性
- 适合需要高动态响应的复杂环境飞行任务
非定常空气动力学效应会显著影响飞行器的飞行表现，尤其在剧烈机动和复杂气动环境中。本文提出一种能够推理非定常气动力的实时规划与控制方法。该方法依赖一个轻量级涡粒子模型，经并行化处理以实现GPU加速，并结合采样型策略优化，利用涡粒子模型进行预测性推理。通过仿真与硬件实验验证，采用该非定常气动模型进行重规划，可在存在非定常环境流扰动的情况下，显著改善无人机在激进后失速机动中的表现。
原文摘要 · Abstract (English)
Unsteady aerodynamic effects can have a profound impact on aerial vehicle flight performance, especially during agile maneuvers and in complex aerodynamic environments. In this paper, we present a real-time planning and control approach capable of reasoning about unsteady aerodynamics. Our approach relies on a lightweight vortex particle model, parallelized to allow GPU acceleration, and a sampling-based policy optimization strategy capable of leveraging the vortex particle model for predictive reasoning. We demonstrate, through both simulation and hardware experiments, that by replanning with our unsteady aerodynamics model, we can improve the performance of aggressive post-stall maneuvers in the presence of unsteady environmental flow disturbances.
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