Magnetic continuum robots (MCRs) have become popular owing to their inherent advantages of easy miniaturization without requiring complicated transmission structures. The evolution of MCRs, from initial designs with one embedded magnet to current designs with specific magnetization profile configurations (MPCs), has significantly enhanced their dexterity. While much progress has been achieved, the quantitative index-based evaluation of deformability for different MPCs, which can assist in designing MPCs with enhanced robot deformability, has not been addressed before. Here we use “deformability” to describe the capability for body deflection when an MCR forms different global shapes under an external magnetic field. Therefore, in this paper, we propose methodologies to design and control an MCR composed of modular axially magnetized segments. To guide robot MPC design, for the first time, we introduce a quantitative index-based evaluation strategy to analyze and optimize robot deformability. Additionally, a control framework with neural network-based controllers is developed to endow the robot with two control modes: the robot tip position and orientation ($M_{1}$) and the global shape ($M_{2}$). The excellent performance of the learnt controllers in terms of computation time and accuracy was validated via both simulation and experimental platforms. In the experimental results, the best closed-loop control performance metrics, indicated as the mean absolute errors, were 0.254 mm and 0.626$^\circ$for mode$M_{1}$and 1.564 mm and 0.086$^\circ$for mode$M_{2}$.

Magnetic continuum robots (MCR) have shown great potential in minimally invasive interventions because they can be actively and remotely navigated through complex in vivo environments. However, the deformation capability of current MCRs is limited by fixed magnetization congurations, preventing them from accessing hard-to-reach areas. This is due to the fact that under a global magnetic field, fixed magnetization conguration causes the magnets on the MCRs exposed to coupled magnetic forces and torques, resulting in a lack of controllable degrees of freedom. Here, we introduce a reprogrammable magnetic continuum robot (RMCR) enabled by magnetic reprogramming modules (MRM). Actuated by shape memory alloys, the magnetic moment direction of MRMs can be selectively reprogrammed in real-time and in-situ. Magnetic reprogramming capabilities enable the RMCR to achieve complex shape transformations. Results show that the range of motion in the tip direction of the RMCR increases by 193% compared with regular MCR. Besides, MRMs on the RMCR can achieve active attraction and separation under simple magnetic fields. The reprogramming process of the RMCR is theoretically investigated. A design methodology for MRMs is then proposed and the fabrication process of RMCR is described in detail. Furthermore, a kinematic model of the RMCR is established, simulated, and experimentally validated.

Magnetically-actuated flexible endoscopes (MAFE) have been well used in minimally-invasive surgery because they can be steered by a magnetic field thus more flexible than traditional endoscopes. Model-free and uncalibrated visual-feedback control makes it possible to manipulate MAFE with a magnetic field without external tracking systems. Because no extra sensor is required to obtain position and posture information, the size of MAFE can be made smaller. However, the traditional control method focuses on 2DoF control, which lacks control over the posture of the end of MAFE. This may result in unnecessary contact between MAFE and tissue and cause injury during the advancement of the endoscope. In this letter, we propose algorithms to enhance the pose control of MAFE to 4DoF and 5DoF based on model-free and uncalibrated visual-feedback control. Experiments in structured environments verify that the control algorithms are able to realize 4DoF manual navigation and 5DoF automatic navigation.