让无人机吊挂系统首次实现自主倒飞，突破传统控制瓶颈
ASTER: Attitude-aware Suspended-payload Quadrotor Traversal via Efficient Reinforcement Learning
- 设计状态初始化策略，利用物理一致的逆运动学引导探索
- 在仿真和真实场景中实现精准姿态对齐与复杂轨迹穿越
- 适合研究飞行器控制、强化学习应用的科研人员
四旋翼吊挂系统敏捷机动受其非平滑混合动力学严重制约。尽管无模型强化学习可规避复杂模型的显式微分，但在严格姿态约束下实现倒飞仍面临奖励稀疏的挑战。本文提出ASTER框架，据我们所知，首次实现该系统的自主倒飞。提出混合动力学启发的状态种子（HDSS）策略，通过在绷紧与松弛缆绳阶段进行物理一致的运动学逆推，回溯目标构型。该策略使智能体发现常规探索无法触及的激进机动。大量仿真与真实实验表明，系统具备卓越敏捷性、精确姿态对齐能力，并在复杂轨迹上实现零样本仿真到现实的迁移。
原文摘要 · Abstract (English)
Agile maneuvering of the quadrotor cable-suspended system is significantly hindered by its non-smooth hybrid dynamics. While model-free Reinforcement Learning (RL) circumvents explicit differentiation of complex models, achieving attitude-constrained or inverted flight remains an open challenge due to the extreme reward sparsity under strict orientation requirements. This paper presents ASTER, a robust RL framework that achieves, to our knowledge, the first successful autonomous inverted flight for the cable-suspended system. We propose hybrid-dynamics-informed state seeding (HDSS), an initialization strategy that back-propagates target configurations through physics-consistent kinematic inversions across both taut and slack cable phases. HDSS enables the policy to discover aggressive maneuvers that are unreachable via standard exploration. Extensive simulations and real-world experiments demonstrate remarkable agility, precise attitude alignment, and robust zero-shot sim-to-real transfer across complex trajectories.
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