Magnetic tracking technology shows great promise for applications in medicine and industry. However, it often suffers from diverse and unpredictable interferences in practical applications, such as hard-/soft-iron interferences and sensor saturation, leading to reduced localization accuracy or even tracking failure. Thus, we propose a two-step framework to mitigate the impact of interferences based on MagRobustNet, a UNet-like autoencoder network. In the first step, disjoint mask sets are used in conjunction with MagRobustNet to detect anomalous measurements subject to disturbances. In the second step, the interfered regions are masked, and MagRobustNet is applied again to recover their expected measurements from neighboring normal data. Experimental results from testing in four interference scenarios showed that the proposed method improved the average position accuracy by 76.2%, enhancing the tracking system's anti-interference capability. In addition, the proposed method can indicate the interfered regions, thereby prompting the adjustment of the magnetometer array to an interference-free location and offering a new potential diagnostic method for localizing ingested foreign bodies in clinical practice.

To steer a soft robot precisely in an unconstructed environment with minimal collision remains an open challenge for soft robots. When the environments are unknown, prior motion planning for navigation may not always be available. This paper presents a novel Sim-to-Real method to guide a cable-driven soft robot in a static environment under the Simulation Open Framework Architecture (SOFA). The scenario aims to resemble one of the steps during a simplified transoral tracheal intubation process where a robotic endotracheal tube is guided to the upper trachea-larynx location by a flexible video-assisted endoscope/stylet. In SOFA, we employ the quadratic programming inverse solver to obtain collision-free motion strategies for the endoscope/stylet manipulation based on the robot model and encode the virtual eye-in-hand vision. Then, we associate the anatomical features recognized by the virtual vision and the joint space motion using a closed-loop nonlinear autoregressive exogenous model (NARX) network. Afterward, we transfer the learned knowledge to the robot prototype, expecting it to navigate to the desired spot in a new phantom environment automatically based on its eye-in-hand vision only. Experiment results indicate that our soft robot can efficaciously navigate through the unstructured phantom to the desired spot with minimal collision motion according to what it has learned from the virtual environment. The results show that the average R-squared coefficient between the closed-loop NARX-forecasted and SOFA-referenced robot's cable and prismatic joint space motion are 0.963 and 0.997, respectively. The eye-in-hand visions also demonstrate good alignment between the robot tip and the glottis.

Wearable magnetic tracking systems (MTSs) offer a promising technology for the long-term tracking of wireless-capsule robots within the digestive tract. However, existing wearable MTSs are fixed in size and cannot accommodate patients with diverse abdominal circumferences. To address this limitation, we propose a wearable and reconfigurable MTS. First, we design a reconfigurable sensor array inspired by the structure of bamboo slips, allowing it to conform to the abdominal surface and accommodate individuals with different abdominal circumferences. Next, we formulate a magnetic tracking optimization problem based on the magnetic dipole model and our established kinematic model of the reconfigurable sensor array. Solving the magnetic tracking problem, we achieved outstanding localization accuracy of 1.44$\pm$0.50 mm and 1.07$\pm 0.16^\circ$. Experimental validation demonstrates our proposed system's portability, reconfigurability, and adaptability to varying abdominal circumferences, offering valuable technological means for diagnosing and treating gastrointestinal disorders.

Traditional magnetic tracking approaches based on mathematical models and optimization algorithms are computationally intensive, depend on initial guesses, and do not guarantee convergence to a global optimum. Although fully supervised data-driven deep learning can solve the above issues, the demand for a comprehensive dataset hampers its applicability in magnetic tracking. Thus, we propose an annular magnet pose estimation network (called AMagPoseNet) based on dual-domain few-shot learning from a prior mathematical model, which consists of two subnetworks: PoseNet and CaliNet. PoseNet learns to estimate the magnet pose from the prior mathematical model, and CaliNet is designed to narrow the gap between the mathematical model domain and the real-world domain. Experimental results reveal that the AMagPoseNet outperforms the optimization-based method regarding localization accuracy (1.87$\pm$1.14 mm, 1.89$\pm \text{0.81}^{\circ }$), robustness (nondependence on initial guesses), and computational latency (2.08$\pm$0.02 ms). In addition, the six-degree-of-freedom pose of the magnet could be estimated when discriminative magnetic field features are provided. With the assistance of the mathematical model, the AMagPoseNet requires only a few real-world samples and has excellent performance, showing great potential for practical biomedical and industrial applications.

High-precision pose adjustment for the self-charging of mobile robots remains a significant challenge. Permanent magnet (PM)-based magnetic tracking technique is a promising technical solution, with occlusion-free and simultaneous positioning and orientation tracking. However, the superposition of the geomagnetic vector and the magnetic field vector generated by the PM leads to the degrading of magnetic tracking performance. Thus, a magnetic tracking technique with real-time geomagnetic vector separation is investigated in this study. Firstly, the environmental magnetic field is accurately modeled, consisting of the PM field, uniform disturbance field, and non-uniform disturbance field. For the uniform disturbance field, we combine it with the PM pose as unknown parameters to be estimated. For the non-uniform disturbance field, a robust kernel function is employed to diminish its influence on positioning performance. Finally, the PM pose and geomagnetic vector are simultaneously estimated by optimization algorithms. A docking experiment for self-charging mobile robots was carried out based on the proposed tracking technique. The robot can successfully recharge its battery with only one alignment operation, where the repeat parking accuracy at the anchor point is 1.38 mm and ±1.27°, respectively.

Navigation accuracy and robustness are key performance indexes of automated guided vehicles (AGVs). In our previous study, the magnetic guidance approach based on magnetic dipole model and non-linear optimization algorithm was proposed, which has high positioning accuracy and could estimate the yaw angle of AGV directly. However, the localization accuracy of the magnetic guidance approach will deteriorate if the magnetic nails (MNs) buried in the ground have installation errors or the magnetic moments between the MNs are inconsistent. To overcome this problem, we propose an improved method based on error analysis and prior knowledge for the magnetic guidance approach. Firstly, the factors that affect the localization accuracy are analyzed, and the parameters ($B_{\mathrm {T}}$,$p$,$c$), whose errors will deteriorate the localization accuracy, are combined with MN pose ($a$,$b$,$m$,$n$) as optimization variables. Then, the prior knowledge regarding ($B_{\mathbf {T}}$,$p$,$c$) is employed to construct the constraint conditions for the magnetic guidance approach. Finally, the global convergence probability and convergence speed of the improved magnetic guidance approach are analyzed. Experimental results demonstrate the adaptability and robustness of the improved magnetic tracking approach, which diminishes the impact of MN installation errors and magnetic moment deviation. The parking accuracy of AGV is improved to 1.42±0.85 mm and 1.10±0.38°.