Caterpillar-type robots are widely used for medium- and large-sized pipe inspections. However, existing prototypes smaller than 80 mm lack both an active variable diameter capability and a contact force sensing function, which are crucial for safe and automatic exploration of unknown rigid/soft pipes (e.g., the colon). This study develops a variable diameter caterpillar-type miniature robot (VCMR) featuring a small size of Φ34.6 mm × 41 mm, a large variable-diameter range of 34.6-89.6 mm, and an integrated contact force sensing function. The VCMR actively adapts to pipe diameter changes using contact force feedback, demonstrates high load capacity in both vertical and horizontal rigid/soft pipes, and traverses a 150-cm colon phantom with sharp bends at an average velocity of 3.07 ± 0.48 cm/s. It holds promise for exploring tiny variable-diameter rigid/soft pipes and delivering cargoes through such pipes.

Magnetically actuated robots have recently shown great capabilities for remote applications in medical procedures. However, the efficient actuation of magnetic robots with dexterous field and gradient generation in large workspaces remains challenging. To overcome the critical challenges, we report a reconfigurable electromagnetic actuation system (REMA) for regulating magnetic fields (maximum: 17 mT) and gradients (maximum: 120 mT/m) in large workspaces. Reconfigurable coil configurations are achieved by employing three mobile electromagnetic coils mounted on three independent 6-DOF robotic arms. Furthermore, the field characteristics generated by a single coil and three coils were modeled via Finite-element method (FEM) and measurements from experiments, respectively. Since there are non-linearities between desired field generation and coil configuration, we propose a multi-objective optimization (MOO) method for generating the Pareto-optimized coil configuration to achieve field and force control in large workspaces. Finally, extensive experiments were conducted to demonstrate the capability and dexterity of our system for autonomous magnetic manipulation in large workspaces, thus showing its potential for clinical applications. Note to Practitioners—This paper aims to address the dexterous generation of magnetic fields and gradients in large workspaces, aiming to realize accurate, efficient, and automated control of different magnetic robots. This paper introduces a reconfigurable electromagnetic actuation system based on three independent robotic arms with three electromagnetic coils. Subsequently, we propose a multi-objective optimization (MOO) method to regulate the coil configuration for generating different fields and gradients. This approach facilitates the application of magnetically driven helical robots, catheters, and capsule robots in various medical scenarios. The results demonstrate that our proposed platform and optimization strategy can effectively implement magnetic manipulations across diverse application scenarios. Looking ahead, we anticipate integrating our work with medical imaging devices to furnish doctors with enhanced tools for medical applications.

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.

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.

Remote-actuated magnetic robots, relying solely on the magnetic torque stemming from rotating magnetic fields, hold immense promise in biomedical applications. However, to precisely actuate magnetic robots in large workspaces, the efficient generation of isotropic rotating fields using electromagnetic actuation (EMA) systems presents an enduring challenge. This is because the choice of configuration of the EMA system is a major concern, particularly when considering collision avoidance between coils and the human body while ensuring isotropic actuation. In this study, we presented an analysis of the characteristics of various three-coil configurations by quantitatively evaluating field isotropy. Furthermore, we introduced a performance-guided optimization method to adjust coil configurations by optimizing designed evaluation metrics, aiming to generate rotating fields with isotropic characteristics in a target local region. Finally, we implemented a reconfigurable EMA and conducted extensive experiments to demonstrate the capability of our method and platform. The experimental results showcase the potential of our approach for advanced clinical applications.