EAGER: Toward Magnetic Manipulation of Nonmagnetic Objects

在过去的十年中，研究人员在使用磁铁精确移动物体而无需任何物理接触方面取得了重大进展。然而，这些物体被特别设计成主要由铁磁材料制成，以便它们可以通过磁铁移动。但是，如果有可能使用磁铁来操纵根本不含磁性材料的金属物体，这在许多工程设备中很常见，那会怎么样呢？众所周知，移动的磁体产生电场，该电场又可以通过产生涡电流在金属物体中产生力。 该项目探讨了如何利用这些力来控制这些物体的运动，使微重力和海底操纵成为可能，并为科学研究提供新的工具。该项目系统地描述了非磁性但导电球体（实心和薄壁）的磁操纵物理特性。对于用于在球体中产生涡流的时变磁场，使用旋转磁偶极子场，其由主要研究者在NSF的支持下开发的独特电磁和永磁硬件平台产生。该项目的特点是涡流为基础的操作作为一个机器人的操作问题，以确定如何的数量和位置的偶极子驱动源影响可操作性。该项目测试的假设，一个领域可以操纵有限的先验知识，通过学习模型？的参数，同时使用防护操作以避免失去控制权限。这种联合学习和控制问题被制定为一个信息理论，主动学习问题，以找到信息样本的学习，受到未知的操作约束所施加的电磁物理系统。最后，该项目还包括对这一现象实际应用的可行性研究。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Over the past decade, researchers have made significant advances on using magnets to precisely move objects without any physical contact. However, the objects are specially designed to be made mostly of ferromagnetic material so that they can be moved by magnets. But what if it were possible to use magnets to manipulate metal objects that contain no magnetic material at all, which is common in many engineered devices? It is well known that moving magnets create electrical fields that, in turn, can generate forces in metal objects through the generation of eddy currents. The project explores how these forces can be used to control the movement of such objects, enabling manipulation in microgravity and undersea, and enabling new tools for scientific research.This project methodically characterizes the physics of magnetic manipulation of nonmagnetic-but-conductive spheres (solid and thin walled). For the time-varying magnetic fields used to generate eddy currents in the spheres, rotating magnetic dipole fields are used, which are generated by unique electromagnetic and permanent-magnet hardware platforms developed by the Principal Investigator with prior NSF support. The project characterizes eddy-current-based manipulation as a robotic-manipulation problem, in order to determine how the number and placement of the dipole actuation sources affects manipulability. The project tests the hypothesis that a sphere can be manipulated with limited prior knowledge by learning the model?s parameters while using guarded actions to avoid loss of control authority. This joint learning and control problem is formulated as an information-theoretic, active-learning problem to find informative samples for learning, subject to unknown manipulation constraints imposed by the electromagnetic physics of the system. Finally, the project includes a feasibility study in the practical application of this phenomenon.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.