用强化学习让无人机悬吊负载快速穿越门洞，比传统方法快3倍。
FLARE: Agile Flights for Quadrotor Cable-Suspended Payload System via Reinforcement Learning
- 通过强化学习直接从仿真中学导航策略，避开复杂优化计算。
- 穿越门洞时速度比顶尖优化方法快3倍，且在真实飞行中成功零样本迁移。
- 可在机载电脑实时运行，适合高动态、高安全要求的无人机任务。
四旋翼悬吊负载系统的敏捷飞行因系统欠驱动、高度非线性及混合动力学而极具挑战。传统基于优化的方法常因计算成本高及缆绳模式转换复杂，难以实现实时应用与机动性发挥。本文提出FLARE，一种直接从高保真仿真中学习敏捷导航策略的强化学习框架。在三个设计的挑战性场景中验证，其在穿越门洞任务中相较最先进的优化方法实现3倍提速。此外，所学策略成功实现零样本仿-实迁移，在真实实验中表现出卓越的敏捷性与安全性，且可在机载计算机上实时运行。
原文摘要 · Abstract (English)
Agile flight for the quadrotor cable-suspended payload system is a formidable challenge due to its underactuated, highly nonlinear, and hybrid dynamics. Traditional optimization-based methods often struggle with high computational costs and the complexities of cable mode transitions, limiting their real-time applicability and maneuverability exploitation. In this letter, we present FLARE, a reinforcement learning (RL) framework that directly learns agile navigation policy from high-fidelity simulation. Our method is validated across three designed challenging scenarios, notably outperforming a state-of-the-art optimization-based approach by a 3x speedup during gate traversal maneuvers. Furthermore, the learned policies achieve successful zero-shot sim-to-real transfer, demonstrating remarkable agility and safety in real-world experiments, running in real time on an onboard computer.
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