用强化学习让轻量无人机机械臂精准抓物，抗干扰能力强。
Global End-Effector Pose Control of an Underactuated Aerial Manipulator via Reinforcement Learning
- 用PPO强化学习生成无人机加速度和关节目标，实现六自由度控制
- 实验达厘米级定位、度级姿态精度，能推重物抗干扰
- 适合做简单轻量级空中操作的机器人研发者参考
将轻量2自由度机械臂通过差速机构安装于四旋翼无人机上，实现六自由度末端执行器位姿控制。该最小化设计虽降低重量与复杂度，但带来欠驱动和对外部扰动敏感的问题。为此，我们在仿真中训练近端策略优化（PPO）智能体，生成无人机加速度与机体角速率的前馈指令及关节角度目标；这些指令由增量非线性动态逆（INDI）姿态控制器和PID关节控制器分别跟踪。飞行实验表明，系统在外部扰动下仍保持厘米级位置精度和度级姿态精度，可完成重载操作与推压任务。结果证明，基于学习的控制策略使轻量平台具备丰富接触交互能力。视频演示见https://youtu.be/bWLTPqKcCOA。
原文摘要 · Abstract (English)
Aerial manipulators, which combine robotic arms with multi-rotor drones, face strict constraints on arm weight and mechanical complexity. In this work, we study a lightweight 2-degree-of-freedom (DoF) arm mounted on a quadrotor via a differential mechanism, capable of full six-DoF end-effector pose control. While the minimal design enables simplicity and reduced payload, it also introduces challenges such as underactuation and sensitivity to external disturbances. To address these, we employ reinforcement learning, training a Proximal Policy Optimization (PPO) agent in simulation to generate feedforward commands for quadrotor acceleration and body rates, along with joint angle targets. These commands are tracked by an incremental nonlinear dynamic inversion (INDI) attitude controller and a PID joint controller, respectively. Flight experiments demonstrate centimeter-level position accuracy and degree-level orientation precision, with robust performance under external force disturbances, including manipulation of heavy loads and pushing tasks. The results highlight the potential of learning-based control strategies for enabling contact-rich aerial manipulation using simple, lightweight platforms. Videos of the experiment and the method are summarized in https://youtu.be/bWLTPqKcCOA.
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