用强化学习让四轴飞行器空中变形落地，提升敏捷性。
Quadrotor Morpho-Transition: Learning vs Model-Based Control Strategies
- 端到端强化学习训练飞行器空中变形落地策略。
- 考虑电机动态和观测延迟后，算法才能成功部署到硬件。
- 适合研究敏捷飞行控制与机器人形态变化的团队参考。
四轴飞行器形态转换（即通过空中变形从空中切换至地面）涉及复杂的空气动力学交互，且需在执行器接近饱和状态下运行，使控制器设计复杂化。现有模型基于控制方法受限于未建模动态以及接触规划需求。本文训练一个端到端强化学习（RL）控制器以学习形态转换策略，并成功实现硬件转移。结果表明，RL控制策略可实现敏捷着陆，但仅在考虑电机动态和观测延迟时才能在硬件上有效运行。相比之下，基准模型预测控制（MPC）控制器无需了解执行器动态和延迟即可直接部署，但面对未知执行器故障时恢复能力较弱。本工作为实现需要空中变形的高敏捷飞行器操作提供了更鲁棒的控制路径。
原文摘要 · Abstract (English)
Quadrotor Morpho-Transition, or the act of transitioning from air to ground through mid-air transformation, involves complex aerodynamic interactions and a need to operate near actuator saturation, complicating controller design. In recent work, morpho-transition has been studied from a model-based control perspective, but these approaches remain limited due to unmodeled dynamics and the requirement for planning through contacts. Here, we train an end-to-end Reinforcement Learning (RL) controller to learn a morpho-transition policy and demonstrate successful transfer to hardware. We find that the RL control policy achieves agile landing, but only transfers to hardware if motor dynamics and observation delays are taken into account. On the other hand, a baseline MPC controller transfers out-of-the-box without knowledge of the actuator dynamics and delays, at the cost of reduced recovery from disturbances in the event of unknown actuator failures. Our work opens the way for more robust control of agile in-flight quadrotor maneuvers that require mid-air transformation.
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