Exerting forces and torques instantaneously on rigid magnetic bodies with no physical connection is an attractive feature of magnetic robotics. This demonstrates great potential for manipulating tools that are externally controlled through the use of magnetic fields in minimally invasive surgeries. The magnetic field can be controlled by the application of currents to electromagnets positioned around the surgical site, and the necessary currents for a specific desired manipulation can be derived from magnetic field models. However, the magnetic field generated by electromagnetic coils are highly nonlinear, especially in the vicinity of the magnetic field sources, which complicates the modeling process. While simple dipole models provide a good approximation for these fields far away from the electromagnets, these models tend to be highly inaccurate near the sources. Magnetic surgical applications benefit from models which accurately describe fields and gradients both near and far from the field source. Particularly, since forces and torques decay inversely proportionally with the cube of the distance to the coil, inaccurate modeling near the coil makes large regions near the coil unfit for applications requiring precisely predicted motion. Estimation errors near coils generate inaccuracies in field models that significantly reduce control performance for rigid magnetic bodies. In order to tackle this problem, we utilize Zernike basis functions to analytically represent the nonlinear magnetic field distribution more accurately. The accuracy of the controller is tested experimentally by driving a magnetic surgical suture needle with a length of 22 mm in the MagnetoSuture™ system along a lemniscate trajectory. The magnetic needle's tip position and the needle orientation, autonomously controlled by the proposed controller, shows RMS tracking error of 2.35 mm using typical dipole models and 1.71 mm for the Zernike fitting approach, a 27% improvement in tracking error. This suggests that the use of Zernike basis functions to capture the nonlinearities of the magnetic field may assist in implementing fast and precise autonomous control strategies for magnetic suture needles.

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.

Electromagnetic field gradients generated by magnetic resonance imaging (MRI) devices pave the way to power untethered magnetic robots remotely. This innovative use of MRI devices allows exerting magnetic pulling forces on untethered magnetic robots, which could be used for navigation, diagnosis, drug delivery and therapeutic procedures inside a human body. So far, MRI-powered untethered magnetic robots lack simultaneous position and orientation control inside three-dimensional (3D) fluids, and therefore, their control has been limited to 3-DoF position control. In this paper, we present a path-planning-based 5-DoF control algorithm to steer and control an MRI-powered untethered robot's position and orientation simultaneously in 3D workspaces in fluids. Eventhough the simulation results show that the proposed optimal controller can successfully control the robot for 5-DoF, in the experiments, we observe a reduced 5-DoF controllability due to the robot manufacturing errors, which result in pitch angle to remain at around the neutral pitching angle at the steady state. The proposed controller was evaluated to track four different paths (linear, planar-horizontal, planar-vertical and 3D paths) generated by 3D Bezier curves. The worst-case path-tracking error was observed for 3D path-following experiments. For this case, the position-tracking error was 2.7±1.8 mm, and the orientation-tracking error was 13.5± 28.7 and 3.7± 10.2 degrees for yaw and pitch angles, respectively. The overall path is completed within 19.6 seconds with 23.6 mm overall displacement and 61.2 and 41.2 degrees of yaw and pitch angle rotation, respectively. Such robots can be used in future MRI-powered active imaging, laser surgery and biopsy robots inside a fluid-filled stomach type of organs.

Magnetic resonance imaging (MRI) devices provide magnetic field gradients, which can be used to remotely actuate magnetic robots that may one day carry out medical tasks, such as diagnosis, drug delivery, or therapeutic-laser-based procedures, in addition to the high-resolution tissue images for diagnosis. However, in comparison with magnetic systems that are custom designed solely for magnetic actuation, the magnetic environment of an MRI device is constrained by the requirements for imaging, which reduces the number of active degrees of freedom available for magnetic actuation. Moreover, the current MRI-powered untethered robots are limited to translational magnetic pulling in three dimensions only. In this article, we propose a design for an untethered magnetic robot that can rotate in a three-dimensional liquid volume in magnetic environments, like those in MRI devices. We demonstrate rotational actuation of our robot inside a commercially available MRI gradient coil. Two kinds of near-neutrally buoyant robot designs are proposed, where each design has particular advantages. Design methodologies, analysis of rotational performance, closed-loop orientation control of up to 2.51 rad of orientation change with maximum net displacement of 18.4 mm, and angular velocity control (0.2-0.63 rad/s) of these robot designs are presented.

Magnetic untethered millirobots, which are actuated and controlled by remote magnetic fields, have been proposed for medical applications due to their ability to safely pass through tissues at long ranges. For example, magnetic resonance imaging (MRI) systems with a 3-7 T constant unidirectional magnetic field and 3D gradient coils have been used to actuate magnetic robots. Such magnetically constrained systems place limits on the degrees of freedom that can be actuated for untethered devices. This paper presents a design and actuation methodology for a magnetic millirobot that exhibits both position and orientation control in 2D under a magnetic field, dominated by a constant unidirectional magnetic field as found in MRI systems. Placing a spherical permanent magnet, which is free to rotate inside the millirobot and located away from the center of mass, allows the generation of net forces and torques with applied 3D magnetic field gradients. We model this system in a 3D planar case and experimentally demonstrate open-loop control of both position and orientation by the applied 2D field gradients. The actuation performance is characterized across the most important design variables, and we experimentally demonstrate that the proposed approach is feasible.