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$M_{2}$.

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.

Untethered microrobots possess a promising perspective for micromanipulation applications. With specifically designed morphologies and structures, microrobots are able to perform controllable delivery of target objects. However, the manipulation process still lacks autonomy, to achieve which the mechanism of picking, transporting, and releasing behaviors needs further investigation. In this article, we propose to achieve automated microrobotic manipulation using magnetic microswarms with multimodal morphology. The microswarm is composed of around 11–21 million$\text{Fe}_{3}\mathrm{O}_{4}$nanoparticles (1.0$\text{--}1.8\,\mu$L particle suspension). When exposed to different dynamic magnetic fields, the swarm could exhibit corresponding forms. We realize precise and controllable cargo picking and releasing by exploiting the fluid fields of different swarm forms. In order to quantitatively describe these behaviors, we design a finite-state machine. A super-twisting sliding-mode controller has been formulated for the motion control of swarms. The disturbances are compensated via a disturbance observer. To enable automated micromanipulation in obstructed scenarios, a path planner inspired by rapidly exploring random tree algorithm is designed for path planning when obstacles exist. We also propose an enhanced-genetic algorithm to optimally transport multiple objects to the target position. Experiments demonstrate that our method could effectively transport micro-objects with different sizes and shapes. The precise selectivity of the method is validated when multiple objects exist in the working environment. Finally, the long-distance delivery ability and adaptivity to various friction situations of our strategy are demonstrated. This work explores a concise, untethered, and automated micromanipulation strategy, provides a new automatic tool for micromanipulation tasks, and extends the application potential of swarm microrobotics.

To address the problem of automatic identification of fine-grained fracture types, in this paper, we propose a novel framework using 3D convolutional neural network (CNN) to learn fracture features from voxelized bone models which are obtained by establishing isomorphic mapping from fractured bones to a voxelized template. The network, which is named FractureNet, consists of four discriminators forming a multi-stage hierarchy. Each discriminator includes multiple sub-classifiers. These sub-classifiers are chained by two kinds of feature chains (feature map chain and classification feature chain) in the form of a full m-ary tree to perform multi-stage classification tasks. The features learned and classification results obtained at previous stages serve as prior knowledge for current learning and classification. All sub-classifiers are jointly learned in an end-to-end network via a multi-stage loss function integrating losses of the four discriminators. To make our FractureNet more robust and accurate, a data augmentation strategy termed r-combination with constraints is further proposed on the basis of an adjacency relation and a continuity relation between voxels to create a large-scale fracture dataset of voxel models. Extensive experiments show that the proposed method can recognize various fracture types in patients accurately and effectively, and enables significant improvements over the state-of-the-arts on a variety of fracture recognition tasks. Moreover, ancillary experiments on the CIFAR-10 and the PadChest datasets at large scales further support the superior performance of the proposed FractureNet.

This paper presents a heterogeneous network interconnection model based on a kind of translucent routing gateway and Linux virtual device to tackle with the communication issue about heterogeneous communication between various devices and Internet in the vehicular networks. In this model, an in-vehicle CAN node is regarded as a usual node in the Internet and will be assigned an IPv6 address and a MAC address as well. This mechanism will make the external terminals connect to these nodes without the perception of the in-vehicle gateway, which will implement the remote condition monitoring and fault diagnosis on a specified unit in the in-vehicle networks. Experiment result shows that this model may establish the communication between the nodes and various devices in the heterogeneous networks.