EAGER: Toward Magnetic Manipulation of Nonmagnetic Objects

在过去的十年里，研究人员在使用磁铁在没有任何物理接触的情况下精确移动物体方面取得了重大进展。然而，这些物体是专门设计的，主要由铁磁材料制成，这样它们就可以通过磁铁移动。但是，如果可以使用磁铁来操纵完全不含磁性材料的金属物体，这在许多工程设备中是常见的，会发生什么呢？众所周知，移动的磁铁产生电场，而电场又可以通过产生涡流在金属物体上产生力。该项目探索如何利用这些力来控制此类物体的运动，实现微重力和海底操作，并为科学研究提供新的工具。该项目有条不紊地描述了非磁性但导电球体(实心和薄壁)的磁性操纵的物理特征。对于用于在球体中产生涡流的时变磁场，使用旋转磁偶极子磁场，该磁场由首席调查员在之前的NSF支持下开发的独特的电磁和永久磁铁硬件平台产生。该项目将基于涡流的操纵描述为机器人操纵问题，以确定偶极激励源的数量和位置如何影响可操纵性。该项目通过学习模型的S参数，同时使用谨慎的动作来验证这样的假设，即可以在有限的先验知识下操纵球体，以避免失去控制权。这种联合学习和控制问题被描述为一个信息论的主动学习问题，目的是在系统电磁物理施加的未知操作约束下，寻找用于学习的信息样本。最后，该项目包括一项实际应用这一现象的可行性研究。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.