用气流远程操控多个物体，实现精准路径追踪与分拣。
Remote Manipulation of Multiple Objects with Airflow Field Using Model-Based Learning Control
- 结合解析模型与学习算法，预测气流场及物体运动
- 在米级距离上成功控制单个和多个物体运动
- 适合需要非接触操作的工业自动化场景
非接触式操纵是机器人领域极具前景的方法，具有广泛的科学与工业应用价值。其中，气流因其可远距离作用且能灵活驱动不同材质、尺寸和形状的物体而备受关注。然而，由于气流场的非线性与随机性，远距离预测其分布及物体运动仍具挑战。本文提出一种基于模型的学习控制方法，利用喷射气流场实现表面多物体的远程操控。该方法融合气流场的解析模型、通过鲁棒系统辨识算法学习的物体动力学，以及模型预测控制器。模型可预测指定喷嘴方向下的无限平面气流速度场；通过学习得到的物体动力学，实现对单个或多个物体在米级距离上的自动远程控制，完成路径跟踪、聚集和分类等任务。
原文摘要 · Abstract (English)
Non-contact manipulation is a promising methodology in robotics, offering a wide range of scientific and industrial applications. Among the proposed approaches, airflow stands out for its ability to project across considerable distances and its flexibility in actuating objects of varying materials, sizes, and shapes. However, predicting airflow fields at a distance-and the motion of objects within them-remains notoriously challenging due to their nonlinear and stochastic nature. Here, we propose a model-based learning approach using a jet-induced airflow field for remote multi-object manipulation on a surface. Our approach incorporates an analytical model of the field, learned object dynamics, and a model-based controller. The model predicts an air velocity field over an infinite surface for a specified jet orientation, while the object dynamics are learned through a robust system identification algorithm. Using the model-based controller, we can automatically and remotely, at meter-scale distances, control the motion of single and multiple objects for different tasks, such as path-following, aggregating, and sorting.
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