We propose an optimization algorithm to generate a robust magnetic force against position error to achieve good controllability of the magnetic robot in the region of interest of a magnetic navigation system comprising electromagnets. We formulate an optimization problem that minimizes the norm of the spatial derivatives of the magnetic force subject to the constraints of the magnetic flux density and magnetic force. The robust magnetic force generated by the proposed algorithm was compared with conventional pseudo-inverse matrix method and verified through an experiment in which a magnetic robot was successfully steered along a bifurcated glass tube to reach a target position.

Magnetic helical robots (MHRs) actuated by an external magnetic field have been regarded as a possible application to treat occlusive vascular disease. We propose a pecking motion of an MHR to effectively tunnel an occlusive vascular lesion. The pecking motion is composed of a rotational driving motion and a linear impulsive motion of the MHR actuated by alternating rotating magnetic field (ARMF). The minimum magnetic flux density to synchronize the MHR to ARMF was mathematically determined from the equation of motion of the MHR and was experimentally verified. Finally, enhanced tunneling performance of the MHR with the pecking motion was verified by in vitro tunneling experiments with a pseudo-thrombus.

We propose a novel position control method (PCM) for the Robotically Assisted MAgnetic Navigation (RAMAN) system to improve the pushability of the magnetic catheter (MC) by maximizing the magnetic force. We expressed the magnetic force acting on the magnetic catheter as the position and the current flowing through each of the electromagnets of the RAMAN system. Next, we formulated a PCM as an optimization problem to maximize the MF. From the proposed PCM, we can determine the position that generates the maximum MF in the desired direction within the region of interest of the RAMAN as well as the required applied current of each electromagnet. Finally, we performed a navigation experiment for the MC along the aorta to the right coronary artery in a cardiac vascular phantom model, and we showed that the proposed PCM can navigate through the vascular phantom effectively without buckling of the MC.

We developed a resonance control method to generate a high-speed rotating magnetic field in a three-phase magnetic navigation system composed of three electromagnets. The proposed resonance control method calculates the amplitudes and phases of voltages, while the capacitances suppressing the self and mutual inductances of the electromagnets to keep the currents and the magnitude of the rotating magnetic field constant, even if the frequency of the rotating magnetic field increases. Finally, we prototyped the three-phase magnetic navigation system and the variable capacitor module to validate the effectiveness of the proposed resonance control method experimentally.

Magnetic robots actuated by magnetic navigation systems (MNSs) have been studied extensively in medical robotics. MNSs composed of multiple electromagnets (EMs) generate external magnetic fields required to apply torque and force to magnetic robots. However, the fixed EM positions in conventional MNSs hinder the generation of a strong magnetic field in the desired direction. To overcome this limitation, several MNSs have recently been proposed to mechanically control EM positions to maximize magnetic field generation. However, solving the resulting nonlinear problem with respect to EM position requires high computational cost. Here, we introduce a decoupled optimization method that first determines the optimal position of the magnetic robot in the ROI by moving the EM part and then determines the current applied to the MNS. The proposed method integrates particle swarm optimization and linear optimization to optimize the EM position and current combination, thereby maximizing both magnetic flux density and magnetic field gradient. Its effectiveness was verified by comparison with a conventional pseudoinverse method. We also experimentally validated the proposed method using an MNS with eight robotically adjustable EMs. Finally, the overall control process was evaluated through navigation experiments with a magnetic catheter and an untethered magnetic robot in various environments.