Wireless electromagnetic tracking (WEMT) technique offers full six degrees-of-freedom (6-DoF) motion capture without the line-of-sight issue, making it highly suitable for wearable devices and virtual/augmented reality. However, WEMT faces a critical challenge: magnetic field sign ambiguity caused by the inability to calculate the synchronous phase difference between the transmitter and the wireless receiver, which leads to ambiguous solutions or restricts tracking to a single quadrant. Existing solutions typically rely on auxiliary hardware, such as inertial sensors or wireless communication modules, to resolve this ambiguity, increasing system complexity. To address this issue, this study proposes an auxiliary-free dynamic phase reference synthesis (DPRS) methodology based on a synthetic virtual transmitter (SVT), which dynamically generates a phase reference for each transmitting coil from receiver measurements using time-evolving sinusoidal models. Crucially, the SVT integrates real-time calibration based on dominant signal components to suppress phase drift, thereby ensuring long-term stability without wired synchronization. Numerical simulations validate the feasibility of the proposed methodology, demonstrating robustness even under varying signal-to-noise ratios. A long-term operation experiment lasting 1 hour was conducted on a self-developed prototype, demonstrating sustained performance with maximum pose errors of 0.48mm and 0.20°. Dynamic 4-quadrant experiments achieved root-mean-square errors of 6.61mm and 0.84°. The lightweight DPRS algorithm enables broader WEMT applications in human-machine interfaces and movement sciences by resolving magnetic field sign ambiguity.

Wireless Electromagnetic Tracking (WEMT) enables non-line-of-sight (NLoS) pose estimation in robotics but faces accuracy limitations from restricted operational range and environmental interference. This paper proposes a WEMT-inertial fusion system enhanced by a learning-based Interacting Multiple Model (IMMNN) to address these challenges. The framework integrates a multi-transmitter array with a WEMT-IMU fusion tracker, leveraging IMMNN to mitigate performance degradation caused by nonlinear spatial noise and motion uncertainty in dynamic, array-based environments. IMMNN employs a graph attention network to dynamically model spatial correlations among array units, adaptively optimizing state transition probabilities across motion models. A gated recurrent framework further enhances robustness by analyzing residual sequences to suppress transient noise and outliers. Experimental results demonstrate that the proposed system achieves a root-mean-square error (RMSE) of 30.4 mm over an expanded 1.9×1.9 m2operational area. The graph attention mechanism enables adaptive spatial noise suppression and ensures stable tracking under rapid motion and electromagnetic disturbances. By synergizing model-driven filtering with data-driven learning, IMMNN effectively improves accuracy and robustness, advancing high-precision WEMT solutions for complex robotic applications.

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.

Human following for mobile robots has emerged as a promising technique with widespread applications. To ensure psychological comfort while collaborating, coexisting, and interacting with humans, robots need to respect the social space of the target person. In this study, we propose a dual closed-loop human-following control strategy that combines model predictive control (MPC) and impedance control. The outer-loop MPC ensures precise control of the robot’s posture while tracking the target person’s velocity and direction to coordinate the motion between them. The inner-loop impedance controller is employed to regulate the robot’s motion and interaction force with the target person, enabling the robot to maintain a respectful and comfortable distance from the target person. Concretely, the social interaction dynamics characteristics between the robot and the target person are described by human-robot interaction dynamics, which considers the rules of social space. Furthermore, an obstacle avoidance component constructed using behavioral dynamics is integrated into the impedance controller. Experimental results demonstrate the effectiveness of the proposed method in achieving human following and obstacle avoidance without intruding into the intimate zone of the target person.

Social robots have gained widespread attention for their potential to assist people in diverse domains, such as living assistance and logistics transportation. Human-accompanying, i.e., walking side-by-side with a person, is an expected and essential capability for social robots. However, due to the complexity of motion coordination between the target person and the mobile robot, the accompanying action is still unstable. In this study, we propose a human-accompanying control strategy to improve the motion coordination for better practicability of the human-accompanying robot. Our approach allows the robot to adapt to the motion variations of the target person and avoid obstacles while accompanying them. First, a human-robot interaction model based on the separation-bearing-orientation scheme is developed to ascertain the relative position and orientation between the robot and the target person. Then, a human-accompanying controller based on behavioral dynamics and model predictive control (MPC) is designed to avoid obstacles and simultaneously track the direction and velocity of the target person. Experimental results indicate that the proposed method can effectively achieve side-by-side accompanying by simultaneously controlling the relative position, direction, and velocity between the target person and robot.

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.

The barcode navigation based on QR (quick response) codes is widely employed in industrial logistics due to its accurate localization and flexible movement paths. However, the regular repair of damaged barcodes and robot speed control when approaching the barcodes are required. In this study, we presented an improved magnetic spot navigation approach to replace the barcode navigation for automated guided vehicles (AGVs). The fusion of the high-precision magnetic tracking method and odometer based on AGV encoders can overcome the disadvantages of barcode navigation. The magnetic tracking approach provides the AGV pose relative to the nearest magnet spot, instead of the low-precision longitudinal and lateral measurement via a magnetic ruler. Besides, with the benefit of the adaptive weighted fusion algorithm, the distance between the adjacent barcode can be set from 500 to 1000 mm via magnetic spots. Experimental results show that the mean path accuracy and mean magnet spot localization accuracy of the improved magnetic spot navigation were 110 ± 30 mm and 14.5 ± 0.87 mm, respectively. The proposed approach provides a novel possibility for large-area and high-precision navigation in AGVs-based industrial logistics, especially for large outdoor scenarios.

A mobile robot that follows behind humans in structured environments has to face the challenge of full occlusion caused by the walls when the target person makes a turn at the corridor intersections. This may result in short-term, even a permanent loss of the target from the field of view of the Human-Following Robots (HFRs). Concerning this issue, a novel side-by-side following method for HFRs is addressed. In this paper, HFRs detect the legs of target person and different types of corridor intersections using the onboard laser scanner at first. Then, we provide a corridor detector method to cluster the geometric structure constraint between the target and corridor intersections. At last, a Side-by-side Following Leg Tracker (SFLT) is designed by integrating the laser information, in order to increase the visible time of the target person, while the target is turning at the corridor intersections. The corridor detector method and SFLT method have been simulated in MATLAB. Moreover, the approach of side-by-side following has been implemented in the Robot Operating System (ROS) of real-life robots in the corridor environment. The results from simulation and practical experiment show that, by using our method, HFRs were able to successfully follow the human92.0% while a mobile robot meeting potential occlusions at corridor intersections.

Neurologists judge the severity of Parkinsonian motor symptoms according to clinical scales, and their judgments exist inconsistent because of differences in clinical experience. Correspondingly, inertial sensing-based wearable devices (ISWDs) produce objective and standardized quantifications. However, ISWDs indirectly quantify symptoms by parametric modeling of angular velocities and linear accelerations nd trained by the judgments of several neurologists through supervised learning algorithms. Hence, the ISWD outputs are biased along with the scores provided by neurologists. To investigate the effectiveness ISWDs for Parkinsonian symptoms quantification, technical verification and clinical validation of both tremor and bradykinesia quantification methods were carried out. A total of 45 Parkinson's disease patients and 30 healthy controls performed the tremor and finger-tapping tasks, which were tracked simultaneously by an ISWD and a 6-axis high-precision electromagnetic tracking system (EMTS). The Unified Parkinson's Disease Rating Scale (UPDRS) prescribed parameters obtained from the EMTS, which directly provides linear and rotational displacements, were compared with the scores provided by both the ISWD and seven neurologists. EMTS-based parameters were regarded as the ground truth and were employed to train several common machine learning (ML) algorithms, i.e., support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF) algorithms. Inconsistency among the scores provided by the neurologists was proven. Besides, the quantification performance (sensitivity, specificity, and accuracy) of the ISWD employed with ML algorithms were better than that of the neurologists. Furthermore, EMTS can be utilized to both modify the quantification algorithms of ISWDs and improve the assessment skills of young neurologists.

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°.

The optical tracking system (OTS) plays a vital role in the computer-assisted surgical navigation process, whereas the performance of the commonly used binocular stereo vision is affected by the line-of-sight problem and limited workspace. Thus, this article proposed a prior knowledge-based multicamera reconstruction model (PKRM) to both expand the tracking workspace and improve the tracking robust and computational efficiency of OTS when working in unstructured clinical conditions. This reconstruction model inherits the advantages of the geometrical method, data-driven method, and gating technique (GT). First, we added the geometric principle as the prior knowledge to optimize the training of the multicamera OTS reconstruction model through the Lagrange multiplier method; hence, the prior knowledge feedforward NN (PKFNN) was built. Second, besides the training features, the state of camera (SOC) was extracted in advance to determine the NN structure using GT. According to the SOC feature, the OTS can be self-adaptive to the changing field of view (FOV) caused by optical occlusion, which is frequently occurred in surgery. Furthermore, experiments were carried out to verify the performance of the proposed model, whose accuracy and runtime performed 0.4627 mm and 0.0016 ms, respectively. Results demonstrate that the proposed reconstruction model can achieve higher accuracy and computational efficiency than both the geometrical model and the data-driven model. Especially, by considering SOC as the state prior knowledge, the tracking robustness is enhanced when one or two of the four cameras are not working properly. Note to Practitioners-The original motivation for this article derives from both the line-of-sight limitation and robust demand for optical tracking of surgical instruments. The performance of the multicamera optical tracking system (OTS) depends on its reconstruction model. However, the geometric reconstruction model requires more calculation to obtain high accuracy, which will enlarge the latency and reduce the update rate. In our previous work, the reconstruction model based on the neural network (NN) has achieved accurate tracking in real-time, while the training of the model tends into local optimal values. Hence, we proposed the prior knowledge feedforward NN model to improve the accuracy and computational efficiency. Moreover, to guarantee the line-of-sight in the optical occlusion, the state of camera combining with the gating technique enables the OTS to be self-adaptive for changing the field of view, which greatly ensures the robust tracking process with larger workspace in case of line-of-sight obstructions.

Deep brain stimulation (DBS) is a crucial surgical procedure for Parkinson's disease and essential tremor. There is yet no designated system for the accurate monitoring of the stimulating effect. Tremor is prominent in the Parkinson's disease. A novel wearable glove system for tremor quantification during DBS is presented. Rest, postural and action tremor assessment tasks are chosen as the feedback to the DBS treatment. Each tremor assessment task lasts for 10 seconds. A total of 5 patients with tremor were tested with the first prototype. Time-frequency analysis and statistical analysis were realized based on the inertial measurement unit signals. Results indicate that the tremor frequency was stable for all patients; however, the tremor amplitude fluctuated all the time. Valid state detection algorithm was performed for each assessment task. The mean tremor amplitudes of valid rest and postural tremor tasks correlate well with the clinical scores. After further experiments and verifications, this system is supposed to support the choosing of the optimal target location and stimulation intensity setting of the DBS electrode during DBS surgery.