让可变桨距无人机在真实世界零样本完成高难度机动
Sim-to-Real Transfer in Reinforcement Learning for Maneuver Control of a Variable-Pitch MAV
- 用系统辨识+领域随机化+课程学习构建鲁棒仿真环境
- 实现无需重训练的零样本部署，完成翻滚与墙后回溯等动作
- 适合做无人机智能控制与仿真实战迁移的研究者参考
强化学习算法可提升无人飞行器的高机动性，但将算法从仿真迁移到真实场景仍具挑战。可变桨距推进器无人机具备更高敏捷性，但其复杂动力学增加了仿真到现实的迁移难度。本文提出一种新型强化学习框架，使可变桨距无人机在真实环境中完成复杂空中机动。方法包括：通过系统辨识、领域随机化和课程学习构建稳健的训练仿真环境；采用级联控制结构结合快速响应底层控制器实现可靠部署。实验结果表明，该框架支持零样本迁移，使无人机成功执行翻滚、墙后回溯等复杂动作。
原文摘要 · Abstract (English)
Reinforcement learning (RL) algorithms can enable high-maneuverability in unmanned aerial vehicles (MAVs), but transferring them from simulation to real-world use is challenging. Variable-pitch propeller (VPP) MAVs offer greater agility, yet their complex dynamics complicate the sim-to-real transfer. This paper introduces a novel RL framework to overcome these challenges, enabling VPP MAVs to perform advanced aerial maneuvers in real-world settings. Our approach includes real-to-sim transfer techniques-such as system identification, domain randomization, and curriculum learning to create robust training simulations and a sim-to-real transfer strategy combining a cascade control system with a fast-response low-level controller for reliable deployment. Results demonstrate the effectiveness of this framework in achieving zero-shot deployment, enabling MAVs to perform complex maneuvers such as flips and wall-backtracking.
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