Enhanced Accuracy in Magnetic Actuation: Closed-loop Control of a Magnetic Agent with Low-Error Numerical Magnetic Model Estimation.

Magnetic actuation holds promise for wirelessly controlling small, magnetic surgical tools and may enable the next generation of ultra minimally invasive surgical robotic systems. Precise torque and force exertion are required for safe surgical operations and accurate state control. Dipole field estimation models perform well far from electromagnets but yield large errors near coils. Thus, manipulations near coils suffer from severe (10×) field modeling errors. We experimentally quantify closed-loop magnetic agent control performance by using both a highly erroneous dipole model and a more accurate numerical magnetic model to estimate magnetic forces and torques for any given robot pose in 2D. We compare experimental measurements with estimation errors for the dipole model and our finite element analysis (FEA) based model of fields near coils. With five different paths designed for this study, we demonstrate that FEA-based magnetic field modeling reduces positioning root-mean-square (RMS) errors by 48% to 79% as compared with dipole models. Models demonstrate close agreement for magnetic field direction estimation, showing similar accuracy for orientation control. Such improved magnetic modelling is crucial for systems requiring robust estimates of magnetic forces for positioning agents, particularly in force-sensitive environments like surgical manipulation.

Overcoming the Force Limitations of Magnetic Robotic Surgery: Magnetic Pulse Actuated Collisions for Tissue-Penetrating-Needle for Tetherless Interventions.

The field of magnetic robotics aims to obviate physical connections between the actuators and end-effectors. Such tetherless control may enable new ultra-minimally invasive surgical manipulations in clinical settings. While wireless actuation offers advantages in medical applications, the challenge of providing sufficient force to magnetic needles for tissue penetration remains a barrier to practical application. Applying sufficient force for tissue penetration is required for tasks such as biopsy, suturing, cutting, drug delivery, and accessing deep seated regions of complex structures in organs such as the eye. To expand the force landscape for such magnetic surgical tools, an impact-force based suture needle capable of penetrating in vitro and ex vivo samples with 3-DOF planar motion is proposed. Using custom-built 14G and 25G needles, we demonstrate generation of 410 mN penetration force, a 22.7-fold force increase with more than 20 times smaller volume compared to similar magnetically guided needles. With the MPACT-Needle, in vitro suturing of a gauze mesh onto an agar gel is demonstrated. In addition, we have reduced the tip size to 25G, which is a typical needle size for interventions in the eye, to demonstrate ex vivo penetration in a rabbit eye, mimicking procedures such as corneal injections and transscleral drug delivery.

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.

Magnetic Resonance Imaging-Compatible Optically Powered Miniature Wireless Modular Lorentz Force Actuators.

Minimally invasive medical procedures under magnetic resonance imaging (MRI) guidance have significant clinical promise. However, this potential has not been fully realized yet due to challenges regarding MRI compatibility and miniaturization of active and precise positioning systems inside MRI scanners, i.e., restrictions on ferromagnetic materials and long conductive cables and limited space around the patient for additional instrumentation. Lorentz force-based electromagnetic actuators can overcome these challenges with the help of very high, axial, and uniform magnetic fields (3-7 Tesla) of the scanners. Here, a miniature, MRI-compatible, and optically powered wireless Lorentz force actuator module consisting of a solar cell and a coil with a small volume of 2.5 × 2.5 × 3.0 mm3 is proposed. Many of such actuator modules can be used to create various wireless active structures for future interventional MRI applications, such as positioning needles, markers, or other medical tools on the skin of a patient. As proof-of-concept prototypes toward such applications, a single actuator module that bends a flexible beam, four modules that rotate around an axis, and six modules that roll as a sphere are demonstrated inside a 7 Tesla preclinical MRI scanner.

Localization and Control of Magnetic Suture Needles in Cluttered Surgical Site with Blood and Tissue.

Real-time visual localization of needles is necessary for various surgical applications, including surgical automation and visual feedback. In this study we investigate localization and autonomous robotic control of needles in the context of our magneto-suturing system. Our system holds the potential for surgical manipulation with the benefit of minimal invasiveness and reduced patient side effects. However, the nonlinear magnetic fields produce unintuitive forces and demand delicate position-based control that exceeds the capabilities of direct human manipulation. This makes automatic needle localization a necessity. Our localization method combines neural network-based segmentation and classical techniques, and we are able to consistently locate our needle with 0.73 mm RMS error in clean environments and 2.72 mm RMS error in challenging environments with blood and occlusion. The average localization RMS error is 2.16 mm for all environments we used in the experiments. We combine this localization method with our closed-loop feedback control system to demonstrate the further applicability of localization to autonomous control. Our needle is able to follow a running suture path in (1) no blood, no tissue; (2) heavy blood, no tissue; (3) no blood, with tissue; and (4) heavy blood, with tissue environments. The tip position tracking error ranges from 2.6 mm to 3.7 mm RMS, opening the door towards autonomous suturing tasks.

Wireless MRI-Powered Reversible Orientation-Locking Capsule Robot.

Magnetic resonance imaging (MRI) scanners do not provide only high-resolution medical imaging but also magnetic robot actuation and tracking. However, the rotational motion capabilities of MRI-powered wireless magnetic capsule-type robots have been limited due to the very high axial magnetic field inside the MRI scanner. Medical functionalities of such robots also remain a challenge due to the miniature robot designs. Therefore, a wireless capsule-type reversible orientation-locking robot (REVOLBOT) is proposed that has decoupled translational motion and planar orientation change capability by locking and unlocking the rotation of a spherical ferrous bead inside the robot on demand. Such an on-demand locking/unlocking mechanism is achieved by a phase-changing wax material in which the ferrous bead is embedded inside. Controlled and on-demand hyperthermia and drug delivery using wireless power transfer-based Joule heating induced by external alternating magnetic fields are the additional features of this robot. The experimental feasibility of the REVOLBOT prototype with steerable navigation, medical function, and MRI tracking capabilities with an 1.33 Hz scan rate is demonstrated inside a preclinical 7T small-animal MRI scanner. The proposed robot has the potential for future clinical use in teleoperated minimally invasive treatment procedures with hyperthermia and drug delivery capabilities while being wirelessly powered and monitored inside MRI scanners.

Shape-programmable magnetic soft matter.

Shape-programmable matter is a class of active materials whose geometry can be controlled to potentially achieve mechanical functionalities beyond those of traditional machines. Among these materials, magnetically actuated matter is particularly promising for achieving complex time-varying shapes at small scale (overall dimensions smaller than 1 cm). However, previous work can only program these materials for limited applications, as they rely solely on human intuition to approximate the required magnetization profile and actuating magnetic fields for their materials. Here, we propose a universal programming methodology that can automatically generate the required magnetization profile and actuating fields for soft matter to achieve new time-varying shapes. The universality of the proposed method can therefore inspire a vast number of miniature soft devices that are critical in robotics, smart engineering surfaces and materials, and biomedical devices. Our proposed method includes theoretical formulations, computational strategies, and fabrication procedures for programming magnetic soft matter. The presented theory and computational method are universal for programming 2D or 3D time-varying shapes, whereas the fabrication technique is generic only for creating planar beams. Based on the proposed programming method, we created a jellyfish-like robot, a spermatozoid-like undulating swimmer, and an artificial cilium that could mimic the complex beating patterns of its biological counterpart.

Design of a bio-inspired pneumatic artificial muscle with self-contained sensing.

Pneumatic artificial muscles (PAMs) are one of the most famous linear actuators in bio-inspired robotics. They can generate relatively high linear force considering their form factors and weights. Furthermore, PAMs are inexpensive compared with traditional electromagnetic actuators (e.g. DC motors) and also inherently light and compliant. In robotics applications, however, they typically require external sensing mechanisms due to their nonlinear behaviors, which may make the entire mechanical system bulky and complicated, limiting their use in simple systems. This study presents the design and fabrication of a low-cost McKibben-type PAM with a self-contained displacement and force sensing capability that does not require any external sensing elements. The proposed PAM can detect axial contraction force and displacement at the same time. In this study, the design of a traditional McKibben muscle was modified to include an inductive coil surrounding the muscle fibers. Then, a thin, soft silicone layer was coated outside of the muscle to protect and hold the sensing coil on the actuator. This novel design measures coil inductance change to determine the contraction force and the displacement. The process can be applied to a variety of existing McKibben actuator designs without significantly changing the rigidity of the actuator while minimizing the device's footprint.