Average Position Error Research Articles

STGRL: SNN based two-stage geomagnetic road localization method

Abstract Geomagnetic navigation is a widely used positioning method capable of correcting the cumulative errors of odometers and inertial navigation systems, thereby ensuring long-distance positioning for vehicles in GPS-denied environments. However, common geomagnetic road navigation algorithms are susceptible to measurement noise, which hinder improvements in positioning efficiency and accuracy. To address this issue, this paper proposes a Siamese Neural Network (SNN) based two-stage geomagnetic road localization method. First, attitude angle information is combined with geomagnetic scalar and vector value to establish geomagnetic reference database to increase the feature dimensions of geomagnetic matching. Then, we use the Random Forest algorithm to perform a coarse matching of the data sequence to determine the current road, balancing the increased computational load resulting from the addition of feature dimensions. Finally, to further reduce the impact of random noise, this paper employs the SNN algorithm based on Transformer Encoder for fine matching of the data sequence. Experiments show that compared to existing methods, the average absolute positioning error of our algorithm has been reduced from 32.36 m to 4.07 m, and the increase in computational load is kept within an acceptable range.

Kinematic calibration of a 5-DOF double-driven parallel mechanism with sub-closed loop on limbs

Abstract This paper proposes a kinematic calibration method of a novel 5-degree-of-freedom double-driven parallel mechanism with the sub-closed loop on limbs. At first, considering the introduction of a sub-closed loop significantly increased the complexity and difficulty of kinematic error modeling, an equivalent transformation method is proposed for the limb with a sub-closed loop. Then kinematic error model of the parallel mechanism is established based on the closed-loop vector method and parasitic motion analysis, which is verified by virtual prototype technology. Because the full kinematic error model is generally redundant, error parameter identifiability analysis is carried out by QR decomposition of the identification Jacobian matrix, and the redundant parameters are removed. Additionally, the Sequence Forward Floating Search algorithm is utilized to optimize measurement configurations to reduce the influence of measurement noise. Finally, with a laser tracker as the measuring device, numerical simulations and experiments are implemented to verify the proposed kinematic calibration method. The experiment results show that average position and orientation errors are reduced from 2.778 mm and 1.115° to 0.263 mm and 0.176°, respectively, within the prescribed workspace.

Design and Modelling of Continuum Robot for Endoscopic Submucosal Dissection Surgery With Lifting Force Estimation.

Endoscopic submucosal dissection (ESD) is an effective treatment for early-stage gastrointestinal cancers. However, traditional surgical instruments lack accuracy and force-sensing. A new type of continuum robot for ESD is designed. An accurate static model of the proposed continuum robot is established, considering cases where the robot bends into C-shapes and S-shapes. A force estimation method based on an accurate static model is proposed. Then, the accuracy of the static model and force estimation is verified through experiments. Finally, an ex-organ experiment is carried out. The average position error of the proposed static model is 0.72mm, accounting for 2.57% of the total robot length. The average error of force estimation is 19.53mN. By gripping and cutting ex-porcine gastric mucosa, the robot's functionality is validated. This paper contributes to precise control and safe interaction of continuum robots.

Pressure distribution based 2D in-bed keypoint prediction under interfered scenes

In-bed pose estimation holds significant potential in various domains, including healthcare, sleep studies, and smart homes. Pressure-sensitive bed sheets have emerged as a promising solution for addressing this task considering the advantages of convenience, comfort, and privacy protection. However, existing studies primarily rely on ideal datasets that do not consider the presence of common daily objects such as pillows and quilts referred to as interference, which can significantly impact the pressure distribution. As a result, there is still a gap between the models trained with ideal data and the real-life application. Besides the end-to-end training approach, one potential solution is to recognize the interference and fuse the interference information to the model during training. In this study, we created a well-annotated dataset, consisting of eight in-bed scenes and four common types of interference: pillows, quilts, a laptop, and a package. To facilitate the analysis, the pixels in the pressure image were categorized into five classes based on the relative position between the interference and the human. We then evaluated the performance of five neural network models for pixel-level interference recognition. The best-performing model achieved an accuracy of 80.0% in recognizing the five categories. Subsequently, we validated the utility of interference recognition in improving pose estimation accuracy. The ideal model initially shows an average joint position error of up to 30.59 cm and a Percentage of Correct Keypoints (PCK) of 0.332 on data from scenes with interferences. After retraining on data including interference, the error is reduced to 13.54 cm and the PCK increases to 0.747. By integrating interference recognition information, either by excluding the parts of the interference or using the recognition results as input, the error can be further minimized to 12.44 cm and the PCK can be maximized up to 0.777. Our findings represent an initial step towards the practical deployment of pressure-sensitive bed sheets in everyday life.

Navigating in Light: Precise Indoor Positioning Using Trilateration and Angular Diversity in a Semi-Spherical Photodiode Array with Visible Light Communication

This research presents a detailed methodology for indoor positioning using visible light communication (VLC) technology, focusing on overcoming the limitations of traditional satellite-based navigation systems. The system is based on an optical positioning framework that integrates trilateration techniques with a semi-spherical array of photodiodes, designed to enhance both positional accuracy and orientation estimation. The system effectively estimates the receiver’s position and orientation with high precision by utilizing multiple white-light-emitting diodes (LEDs) as transmitters and leveraging angular diversity. The proposed method achieves an average position error of less than 3 cm and an angular accuracy within 10 degrees, demonstrating its robustness even in environments with obstructed line of sight. These results highlight the system’s potential for significant indoor positioning accuracy and reliability improvements.

Asynchronous Code Division Multiplexing-Based Visible Light Positioning and Communication Network Using Successive Interference Cancellation Decoding.

In the evolving landscape of sixth-generation wireless communication, the integration of visible light communication (VLC) and visible light positioning (VLP), known as visible light positioning and communication (VLPC), emerges as a pivotal technology. This study addresses the challenges of asynchronous code division multiplexing (ACDM) in VLPC networks, focusing on the enhancement of data transmission quality and positioning accuracy. Firstly, we propose an orthogonal pseudo-random code (OPRC) for ACDM-based VLP systems. Leveraging its excellent correlation properties, VLP signals preserve orthogonality even amidst asynchronous transmissions, achieving sub-centimeter average positioning errors. Next, by combining OPRC with successive interference cancellation decoding (SICD), we propose an enhanced ACDM-based VLPC system that utilizes OPRC for improved signal orthogonality and SICD for progressive elimination of multiple access interference (MAI) among VLPC signals. The results show substantial improvements in bit-error rate (BER) and positioning error (PE), approaching the performance levels observed in synchronized VLPC systems. Specifically, the SICD-OPRC scheme reduces average BER to 4.3 × 10-4 and average PE to 2.7 cm, demonstrating its robustness and superiority in complex asynchronous scenarios.

Prediction and compensation of stretch-bending springback for L-section profile

This study aimed to achieve one-time stretch-bending forming in an L-shaped profile. Consequently, it focused on two main aspects: springback compensation and process parameter optimization. The process first combined simulation and experimental data, through a Co-Kriging model, facilitating the construction of an approximate model of a profile’s stretch-bending forming. A multi-objective optimization model was then formulated, to minimize springback displacement, deformation, and residual stress. The study employed the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to explore the solution space for optimal combinations in stretch-bending processes. It created a single-objective optimization model to minimize the average positional error across each profile node. Subsequently, a simulated annealing algorithm was used to solve this model, enabling the determination of an optimal die profile, which met the required specifications of the one-time stretch-bending forming process. Moreover, a novel variable compensation factor method was proposed, which accurately determined the optimal compensation factors for each node, thereby enhancing node compensation precision.

Modeling and simulation of optical system error transmission in the laser tracker

The optical system of the laser tracker utilizes plane mirrors to construct a reflective path, reducing its size and weight. However, maintaining the alignment of the laser with the ideal optical axis during its propagation in the optical system poses significant challenges in the design, fabrication, and assembly of the optical system. This paper explores the principle of error propagation during the assembly process of the optical system and improves the accuracy of the output laser through a numerical simulation and optimization methods. A general error model for the optical system is established to understand the principle of error propagation. A Monte Carlo numerical simulation and sensitivity analysis are used to study the influence of various errors on the accuracy of the output laser. The machining errors are optimized using a simulated annealing method to balance the manufacturing difficulty and system accuracy. The assembly process is also optimized to reduce the degrees of freedom and the number of optical parts required, and verified by experiments. The experimental results indicate that the average position error of the output laser is 15.743 µm, and the average angle error is 1.427′′. This study provides what we believe is a novel approach and methodology for the design and alignment of optical systems.

Multi-sensor fusion for robust indoor localization of industrial UAVs using particle filter

Abstract Robotic platforms including Unmanned Aerial Vehicles (UAVs) require an accurate and reliable source of position information, especially in indoor environments where GNSS cannot be used. This is typically accomplished by using multiple independent position sensors. This paper presents a UAV position estimation mechanism based on a particle filter, that combines information from visual odometry cameras and visual detection of fiducial markers. The article proposes very compact, lightweight and robust method for indoor localization, that can run with high frequency on the UAV’s onboard computer. The filter is implemented such that it can seamlessly handle sensor failures and disconnections. Moreover, the filter can be extended to include inputs from additional sensors. The implemented approach is validated on data from real-life UAV test flights, where average position error under 0.4 m was achieved.

Spatial Localization of Transformer Inspection Robot Based on Adaptive Denoising and SCOT-β Generalized Cross-Correlation.

In the detection process of the internal defects of large oil-immersed transformers, due to the huge size of large transformers and metal-enclosed structures, the positional localization of miniature inspection robots inside the transformer faces great difficulties. To address this problem, this paper proposes a three-dimensional positional localization method based on adaptive denoising and the SCOT weighting function with the addition of the exponent β (SCOT-β) generalized cross-correlation for L-type ultrasonic arrays of transformer internal inspection robots. Aiming at the strong noise interference in the field, the original signal is decomposed by an improved Empirical Mode Decomposition (EMD) method, and the optimal center frequency and bandwidth of each mode are adaptively searched. By extracting the modes in the frequency band of the positional localization signal, suppressing the modes in the noise frequency band, and reconstructing the Intrinsic Mode Function (IMF) of the independently selected superior modal components, a signal with a high signal-to-noise ratio is obtained. In addition, for the traditional mutual correlation algorithm with a large delay estimation error at a low signal-to-noise ratio, this paper adopts an improved generalized joint weighting function, SCOT-β, which improves the anti-jamming ability of the generalized mutual correlation method at a low signal-to-noise ratio by adding an exponential function to the denominator term of the SCOT weighting function's generalized cross-correlation. Finally, the accurate positional localization of the transformer internal inspection robot is realized based on the quadratic L-array and search-based maximum likelihood estimation method. Simulation and experimental results show the following: the improved EMD denoising method better improves the signal-to-noise ratio of the positional localization signal with a lower distortion rate; in the transformer test tank, which is 120 cm in length, 100 cm in width, and 100 cm in height, based on the positional localization method in this paper, the average relative positional localization error of the transformer internal inspection robot in three-dimensional space is 2.27%, and the maximum positional localization error is less than 2 cm, which meets the requirements of engineering positional localization.

Pose Selection Based on a Hybrid Observation Index for Robotic Accuracy Improvement

The problem of the insufficient accuracy performance of industrial robots in high-precision manufacturing is addressed in this paper. Firstly, a kinematic error model based on an M-DH model was presented. Secondly, a hybrid observability index O6 was proposed to select the optimal poses for parameter identification. O6 is the combination of O1 and O3. The optimal poses were obtained by using the IOOPS algorithm. Thirdly, the fitness function for parameter identification was established, and the Levenberg–Marquardt (LM) algorithm was applied for the accurate identification of kinematic parameter errors. Finally, several experiments were conducted to evaluate the performance of the proposed hybrid observability index O6. The average position error and average attitude error of Staubli TX60 robot were reduced by 89% and 49%. The results show that the proposed hybrid observability index O6 has great stability and effectiveness for robot calibration.

Real-time prediction of postoperative spinal shape with machine learning models trained on finite element biomechanical simulations.

Adolescent idiopathic scoliosis is a chronic disease that may require correction surgery. The finite element method (FEM) is a popular option to plan the outcome of surgery on a patient-based model. However, it requires considerable computing power and time, which may discourage its use. Machine learning (ML) models can be a helpful surrogate to the FEM, providing accurate real-time responses. This work implements ML algorithms to estimate post-operative spinal shapes. The algorithms are trained using features from 6400 simulations generated using the FEM from spine geometries of 64 patients. The features are selected using an autoencoder and principal component analysis. The accuracy of the results is evaluated by calculating the root-mean-squared error and the angle between the reference and predicted position of each vertebra. The processing times are also reported. A combination of principal component analysis for dimensionality reduction, followed by the linear regression model, generated accurate results in real-time, with an average position error of 3.75 mm and orientation angle error below 2.74 degrees in all main 3D axes, within 3 ms. The prediction time is considerably faster than simulations based on the FEM alone, which require seconds to minutes. It is possible to predict post-operative spinal shapes of patients with AIS in real-time by using ML algorithms as a surrogate to the FEM. Clinicians can compare the response of the initial spine shape of a patient with AIS to various target shapes, which can be modified interactively. These benefits can encourage clinicians to use software tools for surgical planning of scoliosis.

Research on a Matching Method for Vehicle-Borne Laser Point Cloud and Panoramic Images Based on Occlusion Removal

Vehicle-borne mobile mapping systems (MMSs) have been proven as an efficient means of photogrammetry and remote sensing, as they simultaneously acquire panoramic images, point clouds, and positional information along the collection route from a ground-based perspective. Obtaining accurate matching results between point clouds and images is a key issue in data application from vehicle-borne MMSs. Traditional matching methods, such as point cloud projection, depth map generation, and point cloud coloring, are significantly affected by the processing methods of point clouds and matching logic. In this study, we propose a method for generating matching relationships based on panoramic images, utilizing the raw point cloud map, a series of trajectory points, and the corresponding panoramic images acquired using a vehicle-borne MMS as input data. Through a point-cloud-processing workflow, irrelevant points in the point cloud map are removed, and the point cloud scenes corresponding to the trajectory points are extracted. A collinear model based on spherical projection is employed during the matching process to project the point cloud scenes to the panoramic images. An algorithm for vectorial angle selection is also designed to address filtering out the occluded point cloud projections during the matching process, generating a series of matching results between point clouds and panoramic images corresponding to the trajectory points. Experimental verification indicates that the method generates matching results with an average pixel error of approximately 2.82 pixels, and an average positional error of approximately 4 cm, thus demonstrating efficient processing. This method is suitable for the data fusion of panoramic images and point clouds acquired using vehicle-borne MMSs in road scenes, provides support for various algorithms based on visual features, and has promising applications in fields such as navigation, positioning, surveying, and mapping.

Configuration reconstruction and all-joint synchronous measurement based on vision for segmented linkage manipulator of rigid-flexible dual-arm space robot

A rigid-flexible dual-arm space robot offers promising potential for on-orbit operation due to complementary strengths of its rigid and flexible manipulators. The rigid manipulator has the advantage of large payload capacity and high motion accuracy. The segmented linkage flexible manipulator is ideal for maneuvering in narrow, unstructured environments like internal satellite inspections and maintenance, due to its flexibility and slender body. However, there are inevitable tracking errors due to the multiple cable-driven mechanisms in the flexible manipulator. It’s difficult to get the configuration and end pose of flexible manipulator without adding external sensors. To address these issues, this paper presents a method for configuration reconstruction and all-joint synchronous measurement of the flexible manipulator. The flexible manipulator is captured by the stereovision system mounted on the rigid manipulator. The links’ equivalent center points are recognized by central moment method and edge line extraction method based on the natural characteristics. Joint-to-link kinematic model of flexible manipulator is established to measure joint angles and reconstruct configuration. Several simulations and experiments are carried out to validate the proposed method. The experimental results showed links’ average measurement position errors were less than 13 mm and joint angles reconstructed using the proposed method had an error of approximately 0.35°. These results demonstrate the accuracy and robustness of the proposed method.

Space interferometer orbit determination with multi-GNSS

Spacecraft positioning plays a crucial role in scientific space mission design, as well as in the further scheduling of scientific observations for such a mission. We examine the application of global navigation satellite systems (GNSS) to solve the orbit determination problem in a pure space very long baseline interferometer (VLBI) project. A network of GPS, GLONASS, Galileo and BeiDou navigation satellites may be a more efficient solution to determining the position and velocity of space radio telescopes and space interferometer baselines. For such a project, it is necessary to take into account the special conditions of GNSS observations, as well as the high accuracy of determining not only position but also velocity. In this paper, we estimate visibility of navigation satellite systems for Low and Medium orbits of a pure space-VLBI system and simulate GNSS code and phase measurements. Orbit determination was performed on smoothed code measurements. Phase measurements simulated with a step of 1 s and combined in Doppler measurements yield average position and velocity errors of 1.01 m and 8.8 mm/s in Medium Earth orbit. The results showed that GNSS observations are sufficient to solve the problems of orbit determination of a space-VLBI interferometer both in Low-Earth and Medium Earth orbits.

MR-PVHEE: A novel magnetic resonance-guided parallel-beam very high-energy electron radiotherapy system

PurposeMR-guided very high-energy electron (VHEE) radiotherapy can combine the imaging advantages of MR with the dosimetry advantages of VHEE radiotherapy, which is of significant clinical value. However, VHEEs are highly susceptible to the Lorentz force generated by the MR magnetic field, and coupling MR and VHEEs is challenging. Here, we present a novel MR-guided parallel-beam VHEE (MR-PVHEE) radiotherapy system and confirm its feasibility and effectiveness. MethodThe proposed MR-PVHEE radiotherapy system includes an ingenious coupling model of MR and VHEEs and an electromagnetic steering parameters commissioning (ESP-COM) method for accurate delivery. First, by generating parallel VHEE (PVHEE) beamlets that point in the same direction as the MR main magnetic field, the coupling model based on triple-stage electromagnetic steering can significantly lessen the twisting and deflecting of the VHEE beamlets' delivery trajectory caused by the MR magnetic field. The dosimetric advantage of VHEEs will be enhanced by delicately utilizing the MR magnetic field to constrain lateral scatter. Then, the ESP-COM method based on Bayesian black-box optimization for the PVHEE beamlets is designed by constructing a cost function using the position and direction information of particles in phase space and optimizing the parameters to deliver the beamlets accurately to the target spots. Finite element electromagnetic modeling and Monte Carlo particle transport simulation are used to validate the MR-PVHEE. ResultsMR-PVHEE can generate the PVHEE beamlets in the same direction as the MR magnetic field and accurately deliver them to the target spots. The average position error is within 0.5 mm, and the average direction error is about 0.5°, which can meet the requirements of clinical treatment accuracy. MR-PVHEE can reduce the VHEE lateral penumbra and increase the dose drop gradient. ConclusionFor the first time, MR and VHEE radiotherapy are successfully coupled in this study, and the innovative MR-PVHEE radiotherapy system can elicit new modalities for MR-guided charged particle radiotherapy and spatially fractionated radiotherapy.

A new industrially magnetic capsule MedRobot integrated with smart motion controller

Capsule medical robots with unique application advantages have broad prospects for gastrointestinal detection, targeted drug delivery, medical assistance, and other fields. However, due to the complexity of the gastrointestinal environment and the limitations of the space magnetic field, robust motion control of capsule robots is still a challenge. Using a permanent magnet (NdFeB) as a space magnetic source, a capsule robot manipulation instrument with an integrated motion intelligent control approach is proposed in this work. First of all, a steering fixture controlling a permanent magnet is designed, and a manipulation instrument capable of five degrees of motion freedom is built with low cost and high accuracy. Furthermore, an integrated motion control approach, consisting of a primary motion subsystem and an auxiliary motion subsystem, is presented, where the motion route of the capsule robot is planned. A parallel optimization strategy is adopted to improve the traditional Gray Wolf Optimization Algorithm, the improved version of which is utilized to tune the parameters of controllers. Finally, some experiments of the designed capsule robot are carried out in different planes and slopes, as well as simulated stomach and real pig stomach, respectively. The results show that the motion characteristics are continuous and stable, and the average position error is 4.2 mm, which meets the application requirements.

A closed-loop calibration method for serial robots based on position constraints and local area measurement

This paper presents a cost-effective and efficient closed-loop calibration method to enhance the absolute positioning accuracy of industrial robots. The method utilizes an economical and highly accurate set of simple measurement devices, including a device mounted on the robot's end effector and a spherical constraint device positioned within the robot's workspace. A novel local area measurement method is employed for the spherical constraint device, effectively converting position errors into the errors in the sphere center position of the constraint device. Furthermore, the error model considers both kinematic parameter deviations of the robot itself as well as those of the measurement device, along with non-kinematic errors caused by link self-weight. The closed-loop calibration model relies on position constraints, enabling identification and compensation of all error parameters using Levenberg Marquardt method after substituting measured data. The effectiveness of this proposed method is demonstrated through simulation and experimentation. In simulations, average position error reduces from 8.249 mm to 0.8384 mm, while absolute mean difference in sphere center positions obtained using our measurement equipment decreases from 1.206 mm to 0.1027 mm. Significant reductions in both position error and difference in sphere center position are indicated after calibration. This proves that the absolute mean value of the sphere center position difference can be used as the basis for judging the size of the robot's end position error. Experimental results show that absolute mean difference in sphere center position decreases from 0.4319 mm to 0.02869 mm, leading to substantial improvement in overall positioning accuracy.

Accuracy of dipole source reconstruction in the 3-layer BEM model against the 5-layer BEM-FMM model.

To compare cortical dipole fitting spatial accuracy between the widely used yet highly simplified 3-layer and modern more realistic 5-layer BEM-FMM models with and without adaptive mesh refinement (AMR) methods. We generate simulated noiseless 256-channel EEG data from 5-layer (7-compartment) meshes of 15 subjects from the Connectome Young Adult dataset. For each subject, we test four dipole positions, three sets of conductivity values, and two types of head segmentation. We use the boundary element method (BEM) with fast multipole method (FMM) acceleration, with or without (AMR), for forward modeling. Dipole fitting is carried out with the FieldTrip MATLAB toolbox. The average position error (across all tested dipoles, subjects, and models) is ~4 mm, with a standard deviation of ~2 mm. The orientation error is ~20° on average, with a standard deviation of ~15°. Without AMR, the numerical inaccuracies produce a larger disagreement between the 3- and 5-layer models, with an average position error of ~8 mm (6 mm standard deviation), and an orientation error of 28° (28° standard deviation). The low-resolution 3-layer models provide excellent accuracy in dipole localization. On the other hand, dipole orientation is retrieved less accurately. Therefore, certain applications may require more realistic models for practical source reconstruction. AMR is a critical component for improving the accuracy of forward EEG computations using a high-resolution 5-layer volume conduction model. Improving EEG source reconstruction accuracy is important for several clinical applications, including epilepsy and other seizure-inducing conditions.

Sensorless Based Haptic Feedback Integration In Robot-assisted Pedicle Screw Insertion For Lumbar Spine Surgery: A preliminary cadaveric study

Pedicle screw fixation is an essential surgical technique for addressing various spinal pathologies, including degenerative diseases, trauma, tumors, neoplasms, and infections. Despite its efficacy, the procedure poses significant challenges, notably the limited visibility of spinal anatomical landmarks and the consequent reliance on surgeon's hand-eye coordination. These challenges often result in inaccuracies and high radiation exposure due to the frequent use of fluoroscopy X-ray guidance. Addressing these concerns, this study introduces a novel approach to pedicle screw insertion by utilizing a robot-assisted system that incorporates sensorless based haptics incorporated 5-DOF surgical manipulation. This innovative system aims to minimize radiation exposure and reduce operating time while improving the surgeon's hand posture capabilities. The developed prototype, expected to be implemented using bilateral control, was tested through preliminary cadaveric experiments focused on the insertion of both percutaneous and open pedicle screws at the L4-L5 level of the lumbar spine. Validation of the Sensorless Haptic Feedback feature was an integral part of this study, aiming to enhance precision and safety. The results, confirmed by fluoroscopic x-ray images, demonstrated the successful placement of two percutaneous and two open pedicle screws, with average position and torque errors of 0.011 radians and 0.054 Nm for percutaneous screws, and 0.0116 radians and 0.0057 Nm for open screws, respectively. These findings underscore the potential of the sensorless haptic feedback in a robot-assisted pedicle screw insertion system to significantly reduce radiation exposure and improve surgical outcomes, marking a significant advancement in spinal surgery technology.