用强化学习让仿鸟扑翼机器人实现敏捷轨迹跟踪
Learning-based Trajectory Tracking for Bird-inspired Flapping-Wing Robots
- 不依赖模型，直接通过强化学习控制高自由度扑翼机
- 能自主切换飞行模式并稳定跟踪多种轨迹
- 适合研究智能飞行控制与仿生机器人的人参考
小型扑翼机器人在复杂环境中具备卓越的灵活飞行潜力，但其扑翼飞行固有的复杂气动特性与高度非线性动力学使得精确轨迹跟踪仍具挑战。本文提出一种无模型的强化学习（RL）控制框架，用于高自由度的仿鸟扑翼机器人，使其具备多模态飞行能力与敏捷轨迹跟踪性能。对包含扑翼系统与RL策略的闭环系统进行了稳定性分析。仿真结果表明，该控制器可成功学习复杂的翼部运动模式，在不同气动条件下实现稳定飞行，并自发切换飞行模式，准确跟踪各类轨迹。
原文摘要 · Abstract (English)
Bird-sized flapping-wing robots offer significant potential for agile flight in complex environments, but achieving agile and robust trajectory tracking remains a challenge due to the complex aerodynamics and highly nonlinear dynamics inherent in flapping-wing flight. In this work, a learning-based control approach is introduced to unlock the versatility and adaptiveness of flapping-wing flight. We propose a model-free reinforcement learning (RL)-based framework for a high degree-of-freedom (DoF) bird-inspired flapping-wing robot that allows for multimodal flight and agile trajectory tracking. Stability analysis was performed on the closed-loop system comprising of the flapping-wing system and the RL policy. Additionally, simulation results demonstrate that the RL-based controller can successfully learn complex wing trajectory patterns, achieve stable flight, switch between flight modes spontaneously, and track different trajectories under various aerodynamic conditions.
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