In remote magnetic navigation, a magnetic navigation system is used to generate magnetic fields to apply mechanical wrenches to steer a magnetic object. This technique can be applied to navigate untethered micro- and nanorobots, as well as tethered magnetic surgical tools for minimally invasive medicine. The design and characterization of these systems have been extensively investigated over the past decade. The determination of the region in space in which these systems can operate has yet to be formalized within the research community. This region is commonly referred to as the “workspace” and constitutes a central concept for any class of robotic system. We focus on magnetic navigation systems comprised of electromagnets and propose a first set of definitions for a magnetic workspace, a methodology to determine it, and evaluation metrics to analyze its characteristics. Our methodology and tools are illustrated with several examples of planar and spatial electromagnetic magnetic navigation systems for both didactic and realistic navigation scenarios.

Remote magnetic navigation is used for the manipulation of untethered micro and nanorobots, as well as tethered magnetic surgical tools for minimally invasive medicine. Mathematical modeling of the magnetic fields generated by magnetic navigation systems is a fundamental task in the control of such tools for biomedical applications. In this article, we describe and compare several existing and newly developed methods for representations of continuous magnetic fields using interpolation in the context of remote magnetic navigation. Clinical-scale electromagnetic navigation systems feature nonlinear magnetization and magnetization interactions between electromagnets, which renders accurate magnetic field modeling challenging. We first introduce a method that can adapt existing linear models to correct for nonlinear magnetization, with similar performance to the current state-of-the-art nonlinear model. Furthermore, we present a method based on convolutional neural networks.

Electromagnetic Navigation Systems (eMNS) can be used to control a variety of multiscale devices within the human body for remote surgery. Accurate modeling of the magnetic fields generated by the electromagnets of an eMNS is crucial for the precise control of these devices. Existing methods assume a linear behavior of these systems, leading to significant modeling errors within nonlinear regions exhibited at higher magnetic fields, preventing these systems from operating at full capacity. In this paper, we use a random forest (RF) and an artificial neural network (ANN) to model the nonlinear behavior of the magnetic fields generated by an eMNS. Both machine learning methods outperformed the state-of-the-art linear multipole electromagnet model (MPEM). The RF and the ANN model reduced the root mean squared error (RMSE) of the MPEM when predicting the field magnitude by approximately 40% and 87%, respectively, over the entire current range of the eMNS. At high current regions, especially between 30 and 35 A, the field-magnitude RMSE improvement of the ANN model over the MPEM was 37 mT, equivalent to 90% error reduction. This study demonstrates the feasibility of using machine learning to model an eMNS for medical applications, and its ability to account for complex nonlinear behavior at high currents. The use of machine learning thus shows promise in developing accurate field predicting models, and ultimately improving surgical procedures that use magnetic navigation.

Retinal diseases including age-related macular degeneration and diabetic retinopathy are leading causes of visual impairment and a growing medical crisis worldwide. As a result, new therapies and methods of safely delivering these new therapies can be of significant benefit to society. In this paper, we present a proof-of-concept for performing drug delivery using flexible magnetic microcatheters. The potential benefits of these catheters are that they are safer to use than existing rigid vitreoretinal tools, are precisely manipulable, and can be used to deliver lower dosages of therapeutic agents to precisely targeted locations on the retina with higher efficacy. The task of positioning the catheter tip is shared between a human operator and a control algorithm, which have complementary skills and can separately address different aspects of the overall task. The system was tested in an eye-phantom by an untrained operator with a 93% success rate in reaching 43 target points uniformly dispersed over the retina.

We introduce the modelling and control of a rolling microrobot. The microrobot is capable of manipulating micro-objects through the use of a magnetic visual control system. This system consists of a rod-shaped microrobot, a magnetic actuation system and a visual control system. Motion of the rolling microrobot on a supporting surface is induced by a rotating magnetic field. As the robot is submerged in a liquid this motion creates a rising flow in front, a sinking flow behind, and a vortex above the robot, thus enabling non-contact transportation of micro-objects. Besides this fluid-vortex approach, the microrobot is also able to manipulate micro-objects via a pushing strategy. We present the design and modelling of the 50×60×300 μm micro-agent, the visual control system, and an experimental analysis of the micromanipulation and control methods.

In this paper, we present a holistic system for automating the motion of a rolling microrobot for protein crystal harvesting. The RodBot, which was introduced in previous work, is able to perform noncontact manipulation of microscopic objects such as fragile crystals by trapping them in an induced vortex fluid flow. Here, we are concerned with navigating the RodBot autonomously in a liquid environment containing obstacles such as crystals. A literature review shows existing approaches to untethered microrobot control are limited and cluttered environments are often not considered. We demonstrate real-time tracking of the RodBot and surrounding obstacles, kinematic obstacle-free path planning, and nonholonomic path following. The system was evaluated in qualitative and quantitative experiments, shows satisfactory performance, and presents itself as a first step towards fully automated crystal harvesting.