Efficient Optimization of a Permanent Magnet Array for a Stable 2D Trap

TL;DR

This paper presents a GPU-accelerated optimization method to design permanent magnet arrays for stable 2D magnetic trapping of millirobots, enabling precise control over large distances relevant to biomedical applications.

Contribution

It introduces a novel GPU-based optimization algorithm for designing permanent magnet arrays that achieve stable 2D magnetic traps, overcoming feedback control challenges.

Findings

Successfully trapped and controlled a millirobot in 2D using the magnet array.

The optimization algorithm can compute optimal magnet angles for 100 magnets in under three seconds.

The method is verified through numerical simulation and physical experiments.

Abstract

Untethered magnetic manipulation of biomedical millirobots has a high potential for minimally invasive surgical applications. However, it is still challenging to exert high actuation forces on the small robots over a large distance. Permanent magnets offer stronger magnetic torques and forces than electromagnetic coils, however, feedback control is more difficult. As proven by Earnshaw's theorem, it is not possible to achieve a stable magnetic trap in 3D by static permanent magnets. Here, we report a stable 2D magnetic force trap by an array of permanent magnets to control a millirobot. The trap is located in an open space with a tunable distance to the magnet array in the range of 20 - 120mm, which is relevant to human anatomical scales. The design is achieved by a novel GPU-accelerated optimization algorithm that uses mean squared error (MSE) and Adam optimizer to efficiently compute the optimal angles for any number of magnets in the array. The algorithm is verified using numerical simulation and physical experiments with an array of two magnets. A millirobot is successfully trapped and controlled to follow a complex trajectory. The algorithm demonstrates high scalability by optimizing the angles for 100 magnets in under three seconds. Moreover, the optimization workflow can be adapted to optimize a permanent magnet array to achieve the desired force vector fields.