Magnetic Manipulation System Research Articles

Robust 3-D Path Following Control Framework for Magnetic Helical Millirobots Subject to Fluid Flow and Input Saturation.

Precise trajectory control is imperative to ensure the safety and efficacy of in vivo therapy employing the magnetic helical millirobots. However, achieving accurate 3-D path following of helical millirobots under fluid flow conditions remains challenging due to the presence of the lumped disturbances, encompassing complex fluid dynamics and input frequency saturation. This study proposes a robust 3-D path following control framework that combines a disturbance observer for perturbation estimation with an adaptive finite-time sliding mode controller for autonomous navigation along the reference trajectories. First, a magnetic helical millirobot's kinematic model based on the 3-D hand position approach is established. Subsequently, a robust smooth differentiator is implemented as an observer to estimate disturbances within a finite time. We then investigate an adaptive finite-time sliding mode controller incorporating an auxiliary system to mitigate the estimated disturbance and achieve precise 3-D path tracking while respecting the input constraints. The adaptive mechanism of this controller ensures fast convergence of the system while alleviating the chattering effects. Finally, we provide a rigorous theoretical analysis of the finite-time stability of the closed-loop system based on the Lyapunov functions. Utilizing a robotically-actuated magnetic manipulation system, experimental results demonstrate the efficacy of the proposed approach in terms of the control accuracy and convergence time.

Tracking of a Magnetically Navigated Millirobot With a Magnetic-Field Camera

Significant progress has been made in the development of magnetic micromanipulation for minimally invasive surgery. The development of systems to localize millimeter-sized robots during magnetic manipulation without line-of-sight detection remains, however, a challenging task. In this study, we focused on the development of a tracking system aiming to fill this gap. A robot which consists of a cylindrical magnet of 1 mm diameter is localized using a 2D array of 3D magnetoresistive sensors. The system, also called magnetic field camera, provides tracking of the robot with a refresh rate of 2 Hz. The developed tracking algorithm reaches a mean absolute error for the position and the orientation of, respectively 0.56 mm and 5.13° in 2D. This system can be added to existing magnetic manipulation systems allowing closed loop control of the navigation. The performances of the magnetic field camera are not affected by an exposure to strong magnetic fields. Exposures up to 3 T have been validated. Increasing the integrability of the magnetic field camera into magnetic manipulation systems. The presented tracking system makes it possible to target applications such as minimally invasive eye surgery or drug delivery. The high spatial and magnetic resolutions allow the tracking of magnetic particles, down to 200 μm diameter, when placed close to the surface. The system could also be suitable for the localization of small objects for 2D biomanipulation.

Magnetic particle image scanner based on asymmetric core-filled electromagnetic actuator

Monitoring the distribution of magnetic nanoparticles (MNPs) in the vascular system is an important task for the advancement of precision therapeutics and drug delivery. Despite active targeting using active motilities, it is required to visualize the position and concentration of carriers that reach the target, to promote the development of this technology. In this work, a feasibility study is presented on a tomographic scanner that allows monitoring of the injected carriers quantitatively in a relatively short interval. The device is based on a small-animal-scale asymmetric magnetic platform integrated with magnetic particle imaging technology. An optimized isotropic field-free region (FFR) generation method using a magnetic manipulation system (MMS) is derived and numerically investigated. The in-vitro and in-vivo tracking performances are demonstrated with a high position accuracy of approximately 1 mm. A newly proposed tracking method was developed, specialized in vascular system, with quick scanning time (about 1s). In this paper, the primary function of the proposed system is to track magnetic particles using a magnetic manipulation system. Through this, proposed method enables the conventional magnetic actuation systems to upgrade the functionalities of both manipulation and localization of magnetic objects.

A novel visual biosensor for mercury detection based on a cleavable phosphorothioate-RNA probe and microfluidic bead trap

Mercury ion (Hg2+) detection plays a crucial role in safeguarding the environment and protecting human health. In this study, we developed a microfluidic biosensing platform that utilizes a phosphorothioate-RNA (PS-RNA) probe and gold-coated magnetic nanoparticles to quantify Hg2+ visually. The PS-RNA probe was linked to gold-coated magnetic nanoparticles via Au–S interaction and biotin–avidin interaction and then combined with polystyrene microspheres to form a self-assembled structure called a magnetic bead-AuNP-probe-polystyrene (MAPP) bead. We designed and fabricated a microfluidic chip that contained only a magnetic separator and a particle trap. The presence of Hg2+ cut the PS-RNA probe, leading to the separation of magnetic beads and polystyrene microspheres. The MAPP was removed by the magnetic separator, while the cut polystyrene microspheres flowed along the channel and accumulated in the particle trap, forming a long visible strip. The length of the polystyrene microsphere strip was proportional to the Hg2+ concentration. The microfluidic biosensing platform exhibited high sensitivity and specificity for Hg2+ detection, with a detection limit of 21.9 nM. By integrating the microfluidic magnetic manipulation system with a sensitive nucleic acid probe, we developed an automated and visual biosensor that is a valuable reference for the on-site visual detection of heavy metal ions.

Fully Integrated Microfluidic Device for Magnetic Bead Manipulation to Assist Rapid Reaction and Cleaning.

Methods to manipulate magnetic beads are essential factors to determine the efficiency and dimension of an in vitro diagnostic system. Currently, using movable permanent magnets and planar electromagnets is still the major approach to achieve magnetic bead control, causing significant constraint in the miniaturization of in vitro diagnostic systems. Here, we propose techniques to construct a fully integrated microfluidic device that can conduct automatic magnetic bead manipulation as well as rapid chemical reaction and cleaning in a minimized dimension similar to a USB disk. The device combines the precision control of multiple electromagnetic coils with the compactness of microfluidic channels, leading to one of the smallest automatic magnetic bead manipulation systems that can complete several major magnetic bead-based operation steps such as sample injection, reaction, cleaning, and collection. The influencing factors such as coil driving parameters, surface treatment of the microchannels, and properties of magnetic particles have also been investigated to optimize the device performance. The device can drive mixtures of Fe3O4 microparticles and polymer magnetic beads (PMBs) with a weight ratio of 1:1 at a maximum speed of 0.5 cm·s-1 and reduce the time for DNA binding and dissociation reactions from 20 min to only 48 s. This device has significantly advanced the conventional manipulation methods of magnetic beads and has demonstrated the possibility to construct an automatic and ultraminiaturized in vitro diagnostic system that may facilitate portable or even wearable chemical analysis.

Toward Physically Plausible Data-Driven Models: A Novel Neural Network Approach to Symbolic Regression

Many real-world systems can be described by mathematical models that are human-comprehensible, easy to analyze and can be helpful in explaining the system’s behavior. Symbolic regression is a method that can automatically generate such models from data. Historically, symbolic regression has been predominantly realized by genetic programming, a method that evolves populations of candidate solutions that are subsequently modified by genetic operators crossover and mutation. However, the approach suffers from several deficiencies: it does not scale well with the number of variables and samples in the training data – models tend to grow in size and complexity without an adequate accuracy gain, and it is hard to fine-tune the inner model coefficients using just genetic operators. Recently, neural networks have been applied to learn the whole analytic model, i.e., its structure as well as the coefficients, by means of gradient-based optimization algorithms. In this paper, we propose a novel neural network-based symbolic regression method that constructs physically plausible models based on very small training data sets and prior knowledge about the system. The method employs an adaptive weighting scheme to effectively deal with multiple loss function terms and an epoch-wise learning process to reduce the chance of getting stuck in poor local optima. Furthermore, we propose a parameter-free method for choosing the model with the best interpolation and extrapolation performance out of all models generated throughout the whole learning process. We experimentally evaluate the approach on four test systems: the TurtleBot 2 mobile robot, the magnetic manipulation system, the equivalent resistance of two resistors in parallel, and the longitudinal force of the anti-lock braking system. The results clearly show the potential of the method to find parsimonious models that comply with the prior knowledge provided.

Magnetic Bead Manipulation in Microfluidic Chips for Biological Application.

Magnetic beads manipulation in microfluidic chips is a promising research field for biological application, especially in the detection of biological targets. In this review, we intend to present a thorough and in-depth overview of recent magnetic beads manipulation in microfluidic chips and its biological application. First, we introduce the mechanism of magnetic manipulation in microfluidic chip, including force analysis, particle properties, and surface modification. Then, we compare some existing methods of magnetic manipulation in microfluidic chip and list their biological application. Besides, the suggestions and outlook for future developments in the magnetic manipulation system are also discussed and summarized.

Multiscale Catalysis Under Magnetic Fields: Methodologies, Advances, and Trends

AbstractMagnetic field‐enhanced catalysis is an advanced strategy for enhancing catalytic reactions that have emerged in recent years, presenting great potential for alleviating the energy crisis and environmental pollution. Under favorable non‐contact magnetic field conditions, the external magnetic field that produces the enhancement effect can provide additional energy to the catalytic system as an additional driving force for the catalytic reaction and thus positively improve the overall catalytic efficiency. Exploring the effects of magnetic fields on multiscale catalytic reactions can broaden the practical applications of various catalytic reactions. This review begins with a brief introduction and analysis of possible mechanisms for magnetic field‐enhanced catalytic reactions (including spin polarization theory and electromagnetic theory), a description of the forces generated by magnetic fields, nano‐ and microscale magnetic materials, and various commonly used magnetic manipulation systems, and an overview of the application of magnetic fields to enhanced photocatalytic, electrocatalytic and biocatalytic reactions. Finally, the challenges and future prospects for advancing magnetic field‐enhanced catalytic reactions are presented. It is hoped that this review will provide a reference for the development and in‐depth study of magnetic field‐assisted enhanced catalytic reactions to improve catalytic efficiency.

Control of Magnetic Surgical Robots With Model-Based Simulators and Reinforcement Learning.

Magnetically manipulated medical robots are a promising alternative to current robotic platforms, allowing for miniaturization and tetherless actuation. Controlling such systems autonomously may enable safe, accurate operation. However, classical control methods require rigorous models of magnetic fields, robot dynamics, and robot environments, which can be difficult to generate. Model-free reinforcement learning (RL) offers an alternative that can bypass these requirements. We apply RL to a robotic magnetic needle manipulation system. Reinforcement learning algorithms often require long runtimes, making them impractical for many surgical robotics applications, most of which require careful, constant monitoring. Our approach first constructs a model-based simulation (MBS) on guided real-world exploration, learning the dynamics of the environment. After intensive MBS environment training, we transfer the learned behavior from the MBS environment to the real-world. Our MBS method applies RL roughly 200 times faster than doing so in the real world, and achieves a 6 mm root-mean-square (RMS) error for a square reference trajectory. In comparison, pure simulation-based approaches fail to transfer, producing a 31 mm RMS error. These results demonstrate that MBS environments are a good solution for domains where running model-free RL is impractical, especially if an accurate simulation is not available.

Optimized Dynamic Motion Performance for a 5-DoF Electromagnetic Manipulation

High precision manipulation and optimization of actuation current are major concerns in the design of magnetic manipulation systems. In this paper, a flexible magnetic field mapping model has been employed to determine the field distribution of a compact electromagnetic manipulation system in 3D space, and validated by a high-resolution magnetic calibration system and Finite Element Analysis, which enables an improved accuracy and performance of magnetic manipulation. In addition, an iterative current optimization scheme is used to optimize the actuation current through Iterated Tikhonov Regularization. Finally, a feedback control algorithm including a Proportional-Integral-Derivative controller and Extended State Observer is conducted to solve general external disturbance during the autonomous manipulation of magnetic objects. By incorporating magnetic field mapping, feedback control, and current optimization model, the accuracy of the system has been demonstrated by navigating a small-size permanent magnet through predefined trajectories (Z-shaped, spiral curve) with the average position error of 0.27 mm (body length of 1 mm) and maximum average velocity of 3.59 mm/s. Meanwhile, the results showed that the required maximum actuation current was reduced from 4.00 A to 1.81 A. Our work provides a reliable and systematic approach for high-precision magnetic manipulation and optimization of actuation currents.

A Flexible Catheter System for Ultrasound-Guided Magnetic Projectile Delivery

Magnetic actuation is a versatiletechnology, widely applied in medical robotics for noncontact steering of flexible instruments and untethered agents. In this article, we exploit the benefits of this technology by developing a magnetically actuated flexible catheter capable of the controlled ejection and retrieval of an untethered magnetic capsule. The catheter is actuated by the advanced robotics for magnetic manipulation system. The scanning ultrasound is used for 3-D shape reconstruction of the catheter, with a mean error of 0.37 mm. We use a closed-loop position controller to steer the catheter, reporting a mean error of 0.82 mm. We develop a dynamic model of the capsule and use it to predict the trajectory of the projectile. We demonstrate the targeting of the capsule utilizing the null-space of the catheter actuation (mean residual tip displacement of 0.8 mm). Finally, we perform a delivery of a capsule to and retrieval from a target inaccessible for the catheter tip, demonstrating the capability of our instrument to reach challenging locations.

Calibration of a multi-mobile coil magnetic manipulation system utilizing a control-oriented magnetic model

In this paper, we address the calibration of a family of magnetic manipulation systems composed of several coils that are moved around by serial robot manipulators. We show in this paper that the calibration of the whole system ultimately results in calibrating the manipulator and coil separately up to an unknown rigid transformation. For calibration of the coil, we propose to use a model that has not been used so far in the literature; a control-oriented model which is sufficiently accurate and computes the magnetic field in real time. A protocol for calibrating the magnetic manipulation system using the Nelder–Mead algorithm to estimate the model parameters is presented. Calibration was performed through simulations and validated experimentally on a physical system. It was observed that the root mean square error was reduced by 37% after calibration of the physical system, indicating an improvement in accurately estimating the magnetic model.

Mobile Ultrasound Tracking and Magnetic Control for Long‐Distance Endovascular Navigation of Untethered Miniature Robots against Pulsatile Flow

Mobile Ultrasound Tracking and Magnetic Control In article number 2100144, Li Zhang and co-workers propose an innovative combination of eye-in-hand ultrasound imaging and mobile-coil magnetic manipulation system to realize real-time tracking and navigation of a helical magnetic robot against pulsatile flow. Two novel algorithms that can overcome the US noises and view lost are presented to ensure the tracking stability and accuracy. This method provides a promising approach for microrobot navigation in the vascular system of the human body.

An Electromagnetically Controllable Microrobotic Interventional System for Targeted, Real-Time Cardiovascular Intervention.

Robotic magnetic manipulation systems offer a wide range of potential benefits in medical fields, such as precise and selective manipulation of magnetically responsive instruments in difficult-to-reach vessels and tissues. However, more preclinical/clinical studies are necessary before robotic magnetic interventional systems can be widely adopted. In this study, a clinically translatable, electromagnetically controllable microrobotic interventional system (ECMIS) that assists a physician in remotely manipulating and controlling microdiameter guidewires in real time, is reported. The ECMIS comprises a microrobotic guidewire capable of active magnetic steering under low-strength magnetic fields, a human-scale electromagnetic actuation (EMA) system, a biplane X-ray imaging system, and a remote guidewire/catheter advancer unit. The proposed ECMIS demonstrates targeted real-time cardiovascular interventions in vascular phantoms through precise and rapid control of the microrobotic guidewire under EMA. Further, the potential clinical effectiveness of the ECMIS for real-time cardiovascular interventions is investigated through preclinical studies in coronary, iliac, and renal arteries of swine models in vivo, where the magnetic steering of the microrobotic guidewire and control of other ECMIS modules are teleoperated by operators in a separate control booth with X-ray shielding. The proposed ECMIS can help medical physicians optimally manipulate interventional devices such as guidewires under minimal radiation exposure.

Control Method in Minimum Infinity-Norm Approach for Multicoil Magnetic Manipulation System

A magnetic manipulation system for medical applications is designed with its coils widely distributed over a sufficiently large space such that a patient can lie down, an operator can enjoy the accessibility, and the workspace is sufficiently large. However, this design makes the current references asymmetrical. Moreover, at the corners, some references might exceed the coil current rating, and the magnetic field cannot be synthesized as desired. To address this overcurrent reference problem, this article proposes a magnetic manipulation system controlled with the minimum infinity-norm current reference. In many magnetic manipulation systems, the current reference for a given motion command of the magnetic field is infinite. And the minimum infinity-norm current reference is the most feasible. The proposed control strategy presents an increased maximum synthesizable magnetic field in an arbitrary direction, including the worst case direction, compared with the conventional field of Moore–Penrose inverse control. To verify the effectiveness, the worst case direction of the magnetic field of each method is analytically derived. And, the controllable areas of the eight-coil magnetic manipulation system are compared. This article also proposes a soft mode transition between the two methods for efficiency enhancement. The experimental results show the effectiveness of the proposed control strategy.

Control of Magnetic Manipulator Using Reinforcement Learning Based on Incrementally Adapted Local Linear Models

Reinforcement learning (RL) agents can learn to control a nonlinear system without using a model of the system. However, having a model brings benefits, mainly in terms of a reduced number of unsuccessful trials before achieving acceptable control performance. Several modelling approaches have been used in the RL domain, such as neural networks, local linear regression, or Gaussian processes. In this article, we focus on techniques that have not been used much so far: symbolic regression (SR), based on genetic programming and local modelling. Using measured data, symbolic regression yields a nonlinear, continuous-time analytic model. We benchmark two state-of-the-art methods, SNGP (single-node genetic programming) and MGGP (multigene genetic programming), against a standard incremental local regression method called RFWR (receptive field weighted regression). We have introduced modifications to the RFWR algorithm to better suit the low-dimensional continuous-time systems we are mostly dealing with. The benchmark is a nonlinear, dynamic magnetic manipulation system. The results show that using the RL framework and a suitable approximation method, it is possible to design a stable controller of such a complex system without the necessity of any haphazard learning. While all of the approximation methods were successful, MGGP achieved the best results at the cost of higher computational complexity. Index Terms–AI-based methods, local linear regression, nonlinear systems, magnetic manipulation, model learning for control, optimal control, reinforcement learning, symbolic regression.

Multi-objective symbolic regression for physics-aware dynamic modeling

Virtually all dynamic system control methods benefit from the availability of an accurate mathematical model of the system. This includes also methods like reinforcement learning, which can be vastly sped up and made safer by using a dynamic system model. However, obtaining a sufficient amount of informative data for constructing dynamic models can be difficult. Consequently, standard data-driven model learning techniques using small data sets that do not cover all important properties of the system yield models that are partly incorrect, for instance, in terms of their steady-state characteristics or local behavior. However, often some knowledge about the desired physical properties of the model is available. Recently, several symbolic regression approaches making use of such knowledge to compensate for data insufficiency were proposed. Therefore, this knowledge should be incorporated into the model learning process to compensate for data insufficiency.In this paper, we consider a multi-objective symbolic regression method that optimizes models with respect to their training error and the measure of how well they comply with the desired physical properties. We propose an extension to the existing algorithm that helps generate a diverse set of high-quality models. Further, we propose a method for selecting a single final model out of the pool of candidate output models. We experimentally demonstrate the approach on three real systems: the TurtleBot 2 mobile robot, the Parrot Bebop 2 drone and the magnetic manipulation system. The results show that the proposed model-learning algorithm yields accurate models that are physically justified. The improvement in terms of the model’s compliance with prior knowledge over the models obtained when no prior knowledge was involved in the learning process is of several orders of magnitude.

Optimal current shell approximation for solenoids of rectangular cross-section

The manipulation of magnetic objects using variable magnetic fields is a growing field of study with a variety of applications. Many magnetic manipulation systems use multiple electromagnets to generate magnetic fields. To control objects in real time, it is necessary to have a computationally efficient method of calculating the field produced by each solenoid anywhere in space. This paper presents a procedure to replace a real cylindrical solenoid of rectangular cross section with infinitely thin shells and rings that generate an equivalent magnetic field. The best approximation for a real solenoid is determined by its physical characteristics. The field produced by these idealized shapes can be calculated expediently using elliptic integrals as can the field gradient and higher-order derivatives. We find that for most real solenoids, the error in the magnetic field approximation is at most 2.5% at a 50% offset and in most cases is much less.

Automatic Noncontact Extraction and Independent Manipulation of Magnetic Particles Using Electromagnetic Needle

Selective and independent manipulation of microparticles is important for a wide range of applications. Compared to other physical principles, magnetic field is promising due to its ability to penetrate most materials and affect only magnetic objects. However, in most noncontact magnetic manipulation systems, all particles in the workspace are moved simultaneously. This article reports an automatic single-source noncontact magnetic manipulation technique that can selectively extract individual magnetic particles from a population of similar particles and then independently manipulate the extracted particles. We use an electromagnetic needle to create a highly localized magnetic field to achieve the local addressability. The motion of single particles is controlled by adjusting the position of the electromagnetic needle using visual servoing, where two control laws, velocity and position control, have been developed. Experimental results show that a predefined velocity vector can be followed accurately with a directional error of 8.5 $^{\circ }$ and a norm error of 5 $\mu \text{m/s}$ . Similarly, a predefined path can be followed with a position error of 0.5 $\mu \text{m}$ . The capabilities of the proposed method has been demonstrated in four cases: selective extraction of a single particle from a population, separation of two magnetic particles with 10.6 $\mu \text{m}$ initial gap, independent manipulation of four particles, and targeted delivery of two particles onto two separate cells.

Reconsidering Six-Degree-of-Freedom Magnetic Actuation Across Scales

Magnetic actuation, also known as magnetic manipulation, refers to the use of controlled magnetic fields, generated by electromagnets or permanent magnets, to impart forces and torques on a remote magnetic object. The magnetic object is typically modeled as a magnetic dipole, affording five-degree-of-freedom (5-DOF) actuation, comprising 3-DOF force and 2-DOF torque. A method was proposed that uses a three-magnet object in which one magnet is used for traditional 5-DOF actuation and two auxiliary magnets achieve the sixth (torque) DOF via a force couple, and this object can still be modeled and controlled as if it exists at a single point in space, as in traditional 5-DOF actuation. We reconsider this method of 6-DOF magnetic actuation. We perform a numerical study of torque that can be generated on a multi-magnet object of varying shape (but assuming that the individual magnetization vectors are coplanar), size, and orientation, using a variety of well conditioned magnetic manipulation systems. We find that, in the limit as the magnetic object is reduced in size, there is an optimal arrangement of the magnetic object, which is invariant to the manipulation system. We find that the sixth DOF comes at a cost to the original 5-DOF, reducing them by 61% when using the optimal magnetic arrangement; this value is also invariant to the manipulation system. Finally, we find that the sixth DOF scales poorly relative to the other five as the size of the object is reduced, but can be relatively large as we consider objects that are large relative to the distances over which they are being actuated.