用强化学习让悬吊无人机完成攀附动作，不扰动原有定位精度。
Learning Swing-up Maneuvers for a Suspended Aerial Manipulation Platform in a Hierarchical Control Framework
- 分层控制框架中，强化学习在低优先级任务的零空间内调整参考点。
- 仿真验证显示，无人机可成功完成摆动上位动作并稳定停靠。
- 适合需要精准定位又需灵活移动的建筑工地机械臂场景。
本文提出一种新型方法，将强化学习（RL）代理与基于模型的控制相结合，实现悬吊式空中操作平台的摆动上位机动。这类平台适用于建筑工地等场景中的起重机任务，摆动上位动作使其能停靠在仅靠推力无法到达的位置。所提方法基于分层控制框架，按任务优先级执行不同指令。强化学习代理用于调整低优先级任务的参考设定点，以完成摆动上位动作，该动作被限制在高优先级任务（如末端执行器位置与姿态保持）的零空间内。通过大量数值仿真验证了方法的有效性。
原文摘要 · Abstract (English)
In this work, we present a novel approach to augment a model-based control method with a reinforcement learning (RL) agent and demonstrate a swing-up maneuver with a suspended aerial manipulation platform. These platforms are targeted towards a wide range of applications on construction sites involving cranes, with swing-up maneuvers allowing it to perch at a given location, inaccessible with purely the thrust force of the platform. Our proposed approach is based on a hierarchical control framework, which allows different tasks to be executed according to their assigned priorities. An RL agent is then subsequently utilized to adjust the reference set-point of the lower-priority tasks to perform the swing-up maneuver, which is confined in the nullspace of the higher-priority tasks, such as maintaining a specific orientation and position of the end-effector. Our approach is validated using extensive numerical simulation studies.
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