Point Error Research Articles

Effects of spatial learning using tactile maps on orientation accuracy by path integration and mental imagery walking in blindfolded sighted people

Purpose: Focusing on individuals with visual impairment, this study investigated the effects of spatial learning using tactile maps on orientation accuracy by path integration in physical and mental imagery walking scenarios. Materials and methods: Twelve blindfolded sighted people learned nonlooping routes with two corners using tactile maps composed of volumetric raised-line elements, then navigated the routes physically and mentally. At four orientation points along the route—the starting point, Corner A, Corner B, and the endpoint—participants indicated the direction of the other points by aligning a raised, tapered rectangle attached to a horizontal digital protractor. Results and Discussion: During the physical and mental walking tasks, the participants’ mean orientation error values, representing the error in angle from the correct orientation, approximated zero for all orientation directions. However, the mean absolute error, i.e. the absolute value of the orientation error, ranged from 12.5° to 32.5° across different orientation points and tasks. As the participants followed the route, the absolute error relative to the next direction of travel increased, and the absolute errors for orientation points they had passed were substantially large. These results indicate that although tactile maps do not always enable precise orientation, they provide navigators with a surveyed spatial understanding that assists orientation through path integration. Furthermore, the mean difference in orientation error between mental and walking tasks measured on the same route for all directions was not significantly different from zero. This suggests that mental imagery walking with the tactile map helps predict orientation performance by path integration in navigators without vision.

Two-Dimensional Differential Positioning with Global Navigation Satellite System Signal Frequency Division Relay Forwarding to Parallel Leaky Coaxial Cables in Tunnel

To address the issue of GNSS receivers being unable to function properly in tunnels due to the loss of Global Navigation Satellite System (GNSS) signals, this paper proposes a two-dimensional differential positioning system for tunnel environments based on dual leaky coaxial (LCX) cables with GNSS signal frequency relay forwarding. The system receives mixed GNSS signals from open environments and utilizes the frequency selection capabilities of the MAX2769E chip to separate and generate radio frequency signals at different frequencies corresponding to GPS, BDS, and GLONASS. These signals are then used to drive three ports of the LCX cables, which are laid in parallel within the tunnel. By leveraging the uniform radiation characteristics of the LCX cables, stable GNSS signal coverage is achieved throughout the tunnel. On the receiving end, the GNSS receiver achieves two-dimensional positioning by utilizing inter-satellite pseudorange differences and reference point error correction. The simulation results indicate that the dual T-shaped radiating LCX cables configuration offers excellent positioning accuracy and noise resistance, achieving meter-level positioning accuracy in tunnel environments.

Advancing Vehicle Trajectory Prediction: A Probabilistic Approach Using Combined Sequential Models

Abstract This paper addresses the critical need to quantify vehicle trajectory uncertainty in autonomous driving under environmental variability. We focus on predicting the posterior distribution of vehicle trajectories over a fixed horizon, given an initial state and a sequence of actions. We propose and compare three approaches: a probabilistic seq2seq model based on stochastic variational Gaussian processes, sequential Monte Carlo simulation with a single-step Gaussian process model, and a hybrid model that leverages the strengths of both methods. Each approach incorporates a baseline vehicle kinematics model to enhance stability and convergence. We evaluate these methods using a dataset generated from the CARLA simulator, assessing both point error metrics and probabilistic prediction metrics. This research introduces novel approaches to quantifying vehicle trajectory uncertainty through various uncertainty quantification techniques, with the goal of improving the safety and reliability of autonomous vehicle control systems.

A model predictive control algorithm for double-hormone artificial pancreas based on optimal three-branch decision switching rules

At present, double-hormone artificial pancreas is a hotspot in the research of regulating blood glucose level; however, there are few studies on switching mechanism of the subsystems for double-hormone artificial pancreas, and most of the existing studies rely on the prior knowledge of application fields. A model predictive control algorithm for double-hormone artificial pancreas based on optimal three-branch decision switching rules is proposed to solve this problem. The blood glucose controllers of insulin subsystems, zero infusion subsystems, and glucagon subsystems are constructed using model predictive control algorithms. Then, using the three-branch decision theory, switching rules between the three subsystems are designed, and the optimal switching threshold value can be solved by the mixed fruit fly algorithm. The algorithm can realize automatic acquisition of the threshold in the three-branch decision switching rules and gets rid of the dependence on the prior knowledge in the field of diabetes research. By the end, the UVa simulation platform is used to conduct experiments on 33 virtual patients and the simulation results verify that the proposed algorithm can control blood glucose effectively within the normal range and can achieve satisfactory performabce in error of tracking set point, safety, operation efficiency, group control quality, and system stability.

Characterization and Inversion Method of Relative Permeability of Emulsion Flooding.

Relative permeability curves are one of the crucial factors that control the behavior of multiphase flow in petroleum reservoirs. Accurate prediction of the relative permeability curve involving emulsification and emulsion transport in emulsion flooding is vital to enhancing heavy oil recovery. In this study, a relative permeability characterization model including the emulsification mechanism and emulsion transport is developed, in which residual oil saturation and end-point permeability for water are parametrized as functions of emulsion concentration and capillary number. Based on the built characterization model, an inversion method (ensemble Kalman filter) is used to estimate the relative permeability curves of emulsion flooding. The inversion results of four sets of emulsion flooding experiments show that the errors of end points between the Johnson-Bossler-Neumann method and inversion method are less than 10%. For the continuous emulsification process in the in situ emulsion flooding tests, the inversed relative permeability curves shift between the upper and the inferior limits of the permeability. The error of residual oil saturation between the inversion and the experiment is less than 10%. In summary, this study demonstrates the feasibility of using the characterization model and inversion method to infer the relative permeability curves of emulsion flooding, which can aid in the predictions of multiphase flow in heavy oil reservoirs.

Impairments in Proprioceptively-Referenced Limb and Eye Movements in Chronic Stroke.

Upper limb proprioceptive impairments are common after stroke and affect daily function. Recent work has shown that stroke survivors have difficulty using visual information to improve proprioception. It is unclear how eye movements are impacted to guide action of the arm after stroke. Here, we aimed to understand how upper limb proprioceptive impairments impact eye movements in individuals with stroke. Control (N = 20) and stroke participants (N = 20) performed a proprioceptive matching task with upper limb and eye movements. A KINARM exoskeleton with eye tracking was used to assess limb and eye kinematics. The upper limb was passively moved by the robot and participants matched the location with either an arm or eye movement. Accuracy was measured as the difference between passive robot movement location and active limb matching (Hand-End Point Error) or active eye movement matching (Eye-End Point Error). We found that individuals with stroke had significantly larger Hand (2.1×) and Eye-End Point (1.5×) Errors compared to controls. Further, we found that proprioceptive errors of the hand and eye were highly correlated in stroke participants (r = .67, P = .001), a relationship not observed for controls. Eye movement accuracy declined as a function of proprioceptive impairment of the more-affected limb, which was used as a proprioceptive reference. The inability to use proprioceptive information of the arm to coordinate eye movements suggests that disordered proprioception impacts integration of sensory information across different modalities. These results have important implications for how vision is used to actively guide limb movement during rehabilitation.

Flexible reamer use to overcome entry point errors in proximal femoral nail application in severe obese intertrochanteric fracture patients

IntroductionProximal femoral nailing (PFN) offers biomechanical benefits for intertrochanteric fractures but can lead to higher complication rates from poor reduction and technique errors, particularly in obese patients. Incorrect entry points may cause reduction loss, iatrogenic fractures, and misplaced lag screws. The study aims to investigate the effect of using an oriented flexible reamer instead of a rigid reamer on clinical and radiological results to obtain a medial entry point and better positioning of the nail in the intramedullary area in obese intertrochanteric fracture patients.Materials and methodsA retrospective analysis was conducted on patients aged 65 years and older who underwent PFN treatment between March 2020 and June 2022 at a single institution, with at least 1-year postoperative follow-up. Patients were divided into two groups: those applied with a flexible reamer and a rigid reamer. Parameters analyzed from postoperative radiographs included tip-apex distance (TAD), calcar-referenced tip-apex distance (CalTAD), reduction quality, femoral neck-shaft angle, and lag screw placement. Complication rates and types were recorded for each group.ResultThe analysis included 91 patients, with 45 treated using a flexible reamer and 46 treated using a rigid reamer. There was no statistical difference between the two groups regarding age, gender, BMI, and AO class distributions of the patients (p > 0.05). The Femur neck shaft angle was significantly higher in the flexible reamer group (p < 0.001). As a result of the reduction types analysis, medial type reduction was significantly higher in the group where the flexible reamer was applied (p < 0.001). The CalTAD was shorter in the Flexible reamer group (p = 0.005). Complications and the need for reoperation were statistically significantly higher in the rigid reamer group (p < 0.048).ConclusionThe oriented flexible reamer reduces application-related errors in patients undergoing proximal femoral nail (PFN) treatment due to intertrochanteric fracture. The oriented flexible reamer technique allows a more medial entry point. Oriented flexible reamer creates enough space on both fracture sides at the level of intertrochanteric fracture to avoid nail pass-related complications.Level of evidenceLevel III, Case-control study.

Adjustable robust optimization approach for SVM under uncertainty

The support vector machine (SVM) is one of the successful approaches to the classification problem. Since the values of features are typically affected by uncertainty, it is important to incorporate uncertainty into the SVM formulation. This paper focuses on developing a robust optimization (RO) model for SVM. A key distinction from existing literature lies in the timing of optimizing decision variables. To the best of our knowledge, in all existing RO models developed for SVM, a common assumption is that all decision variables are decided before the uncertainty realization, which leads to an overly conservative decision boundary. However, this paper adopts a different strategy by determining the variables that assess the misclassification error of data points or their fall within the margin post-realization, resulting in a less conservative model. The RO models where decisions are made in two stages (some before and the rest after the uncertainty resolution), are called adjustable RO models. This adjustment results in a three-level optimization model for which two decomposition-based algorithms are proposed. In these algorithms, after providing a bi-level reformulation, the model is divided into a master-problem (MP) and a sub-problem the interaction of which yields the optimal solution. Acceleration of algorithms via incorporating valid inequalities into MP is another novelty of this paper. Computational results over simulated and real-world datasets confirm the efficiency of the proposed model and algorithms.

A research on improving the precision of laser tracker in alignment based on Pareto improvement principle.

The accuracy of laser trackers in large-scale dimensional metrology is subject to various influencing factors, with instrument positioning being a primary source of error. To address this, a multi-objective optimization algorithm, grounded in the Pareto improvement principle, is proposed. This algorithm is designed to optimize instrument placement, thereby minimizing measurement errors and enhancing the algorithm's efficacy in error reduction. Thereby, this article proved that positioning the laser tracker consistently on one side of the measurement area and aligning its height with the measuring points can effectively reduce the uncertainty of control point errors by up to 50%.

Research on the high-performance computing method for the neutron diffusion spatiotemporal kinetics equation based on the convolutional neural network

Due to the uncertainty of computational results and the lack of interpretability of models in solving physical field equations in current deep learning, this paper designs a convolutional neural network that can be used to solve the neutron diffusion spatiotemporal kinetics equation in polar and cylindrical coordinate systems. This algorithm directly utilizes the macroscopic cross-section of the material without using the lattice homogenization method, replaces the finite volume method with the extended matrices, and solves the extended matrices using the convolutional kernels instead of the iterative algorithms. Taking the simplified Tsinghua High Flux Reactor (THFR) as an example, the feasibility of the algorithm is verified on the PyTorch platform and compared with the calculation results of the source iteration method running on the GPU. The calculation results show that when the number of grids in the radial and axial sections of the simplified THFR model is 804,600 and 3,576,000, respectively, and the algorithm is iterated 3000 times, the normalized power of the convolutional neural network and the source iteration method converges to 10−10, and the maximum point by point error of the neutron flux density of the above two algorithms converges to 10−5. The computational time consumed by the convolutional neural network is approximately 880.64 s and 3729.62 s, which reduces the computational time by 4.66% and 5.05% compared to the GPU parallel accelerated source iteration method, and the former consumes 43.75% less memory compared to the latter. The convolutional neural network is mainly used as the virtual physics engine for the THFR digital twin system, in addition to solving the neutron diffusion spatiotemporal kinetics equation and further improving computational speed. The algorithm directly utilizes the neutron macroscopic cross-section of the material to calculate the neutron flux density distribution without using the lattice homogenization, providing theoretical guidance and algorithm support for developing the high-precision multi-physical field coupling model.

OGLE-IV Period–Luminosity Relation of the LMC: An Analysis Using Mean and Median Magnitudes

The Period–Luminosity (PL) relation for Cepheid variable stars in the LMC is crucial for distance measurements in astronomy. This study analyzes the impact of using the median rather than the mean of the PL relation’s slope and zero-point. It also examines the persistence of the break at approximately 10 days and addresses specification issues in the PL relation model. Using VI-band median and mean magnitudes from the OGLE-IV survey, corrected for extinction, we fit the PL relation employing robust MM-regression, which features a high breakdown point and robust standard errors. Statistical tests and residual analysis are conducted to identify and correct model deficiencies. Our findings indicate a significant change in the PL relation for Cepheids with periods of 10 days or longer, regardless of whether median or mean magnitudes are used. A bias in the zero-point and slope estimators is observed when using median magnitudes instead of mean magnitudes, especially in the V band. By identifying and correcting regression issues and considering the period break, our estimators for the slope and zero-point are more accurate for distance calculations. Comparative analysis of the models for each band quantifies the bias introduced by using median magnitudes, highlighting the importance of considering the Cepheids’ periods for accurate location measure results, similar to those obtained using mean magnitudes.

Error Analysis of Non-Time-Synchronized Lightning Positioning Method

Since the non-time-synchronized lightning positioning method does not rely on the time synchronization of the stations in the positioning system, it eliminates the errors arising from the pursuit of time synchronization and potentially achieves higher positioning accuracy. This paper provides a comprehensive overview of the errors present in the three-dimensional lightning positioning system. It compares the results of traditional positioning methods with those of non-time-synchronized lightning positioning algorithms. Subsequently, a simulation analysis of the positioning errors is conducted specifically for the non-time-synchronized lightning positioning method. The results show that (1) the non-time-synchronized lightning positioning method exhibits greater errors when utilizing two randomly positioned radiation sources for location determination. Consequently, the resulting positioning outcomes only provide a general overview of the lightning discharge. (2) The positioning outcomes resemble those of the traditional method when employing a fixed-coordinate beacon point. However, the errors in the three-dimensional positional coordinates of these fixed-coordinate beacon points significantly impact the deviations in the positioning results. This impact is positively correlated with the positional error of the beacon point, considering both the orientation and magnitude. (3) Similarly to the traditional method, the farther away from the center of the positioning network, the larger the radial error. (4) The spatial position of the selected fixed-coordinate beacon point has little influence on the error.

A multi-point leakage prediction statistical method using Bayesian inference in water distribution networks

ABSTRACT Leakage in water distribution networks (WDNs) not only leads to serious water loss and pipe contamination but also affects residents’ daily water. Accurate localization of leaks in WDN is significant to conserve water resources and reduce economic losses. However, in traditional optimization and verification methods for leak detection, factors such as modeling and pressure monitoring point errors are not taken into account, resulting in the deviation from the actual location of leaks. To address the mentioned issues, this article presents a WDN leakage probability prediction method based on Bayesian inference. The method converts the prior probability of leakage events into posterior probability. Then, by utilizing the posterior probability density function, the uncertainty of modeling and measurement errors in the hydraulic simulation model are quantified. The probability distribution of leakage pipes and leakage quantity within the leakage area is calculated, allowing for the location prediction and corresponding magnitude of leakage. The experimental results indicate that the prediction model can detect unknown quantities of leakage events. By collecting multiple sets of leakage data, it is possible to accurately predict the location and quantity of leaks, enhancing the efficiency of leakage detection in large-scale water supply networks and providing decision-making assistance for water utilities.

Reconciling Global Terrestrial Evapotranspiration Estimates From Multi‐Product Intercomparison and Evaluation

AbstractTerrestrial evapotranspiration (ET) is a vital process regulating the terrestrial water balance. However, significant uncertainties persist in global ET estimates. Focusing on the area between 60°, we performed an intercomparison of 90 state‐of‐the‐art ET products from 1980 to 2014. These products were obtained from various sources or methods and were grouped into six categories: remote sensing, reanalysis, land surface models, climate models, machine learning methods, and ensemble estimates. It is shown that global ET magnitudes of categories differ considerably, with averages ranging from 518.4 to 706.3 mm yr−1. Spatial patterns are generally consistent but with significant divergence in tropical rainforests. Global trends are mildly positive or negative (−0.10 to 0.37 mm yr−2) depending on categories but with distinct spatial variability. Evaluation against site measurements reveals various performances across land cover types; the ideal point error values range from 0.45 to 0.83, with wetlands performing the worst and open shrublands the best. Using the three‐cornered hat method, there are spatial differences in ET uncertainty, with lower uncertainty for ensemble estimates, showing less than 15% relative uncertainty in most areas. The best global ET data set varies depending on the intended use and study region. Distinct spatial patterns of controlling factors across categories have been identified, with precipitation driving arid and semi‐arid regions and leaf area index dominating tropical regions. It is suggested to include advancing precipitation inputs, incorporate vegetation dynamics, and employ hybrid modeling in future ET estimates. Constraining estimates using complementary data and robust theoretical frameworks can enhance credibility in ET estimation.

Research on Intelligent Ventilation System of Metal Mine Based on Real-Time Sensing Airflow Parameters with a Global Scheme

In ventilation systems of metal mines, the real-time measurement of the airflow field and a reduction in pollutants are necessary for clean environmental management and human health. However, the limited quantitative data and expensive detection technology hinder the accurate assessment of mine ventilation effectiveness and safety status. Therefore, we propose a new method for constructing a mine intelligent ventilation system with a global scheme, which can realize the intelligent prediction of unknown points in the mine ventilation system by measuring the airflow parameters of multiple known points. Firstly, the nodal wind pressure method combined with the Hardy–Cross iterative algorithm is used to solve the mine ventilation network, and the airflow parameters under normal operation and extreme working conditions are simulated, based on which an intelligent ventilation training database is established. Secondly, we compared the airflow parameter prediction ability of three different machine learning models with different neural network models based on the collected small-sample airflow field dataset of a mine roadway. Finally, the depth learning method is optimized to build the intelligent algorithm model of the mine ventilation system, and a large number of three-dimensional simulation data and field measurement data of the mine ventilation system are used to train the model repeatedly to realize the intelligent perception of air flow parameters of a metal mine ventilation network and the construction of an intelligent ventilation system. The results show that the maximum error of a single airflow measurement point is 1.24%, the maximum overall error is 3.25%, and the overall average error is 0.51%. The intelligent algorithm has a good model training effect and high precision and can meet the requirements of the research and application of this project. Through case analysis, this method can predict the airflow parameters of any position underground and realize the real-time control of mine safety.

Shear Performance of Prefabricated Steel Ultra-High-Performance Concrete (UHPC) Composite Beams under Combined Tensile and Shear Loads: Single Embedded Nut Bolts vs. Studs

Ultra-high-performance concrete (UHPC) is widely used in precast concrete-steel composite beams because of its beneficial properties, including reduced structural weight, higher flexural rigidity, and reduced tensile crack formation. In comparison to conventional steel-concrete composite beams, steel-UHPC composite beams exhibit superior characteristics, including reduced structural deadweight, enhanced flexural stiffness, and the capacity to withstand tensile cracking. One successful attempt at meeting the current demands for expedited girder engineering is the development of steel-UHPC composite beams with full-depth precast slabs as key components affecting the overall structural performance using dismountable single embedded nut bolts (SENBs) and widely used studs as competitive alternatives. In contrast, shear connectors are exposed to a combined tensile and shear stress in service life rather than shear only. The corresponding scientific problem is the problem of combined effects under stress in practical applications, but there is currently no relevant research. The shear performance of SENBs in precast steel-UHPC composite beams under tension and shear loads remains unclear. For this purpose, ten push-out specimens and theoretical analyses were performed in this paper, considering the influence of the connector’s type and tensile-to-shear ratio. However, ten specimens were conducted to investigate the tensile-to-shear ratio, and the connector’s type on shear performance is limited. In the future, an increasing number of specimens and test parameters should be considered to investigate the shear performance of precast steel-UHPC composite beams. An increase in the tension-to-shear ratio resulted in a substantial reduction in the ultimate shear capacity, initial shear stiffness, and ductility of the studs. The increase in the tensile-shear ratio from 0 to 0.47 resulted in a 16.9% decline in the ultimate shear capacity, a 30.4% reduction in the initial shear stiffness, and a 21.7% decrease in the ductility of the Series I samples. However, an increase in the tensile-to-shear ratio of the Series II samples from 0 to 0.47 resulted in a 31.3% decline in ultimate shear strength, a 33.2% decline in initial shear stiffness, and a 41.9% decline in ductility. The SENBs demonstrated minimal deviations in ultimate shear capacity compared to their stud counterparts, despite exhibiting notable differences in shear stiffness, and ductility. A lower tensile-to-shear ratio was recommended in practical engineering, which might achieve a larger ultimate shear capacity, stiffness, and ductility. The design-oriented models with enhanced applicability were developed to predict the tension-shear relationship and the load-slip curve of SENBs in prefabricated steel-UHPC composite beams subjected to combined tensile and shear loads. For a tensile-shear relationship model, the point error range was 0 to 0.08, with an average error of 0.03. The square coefficient (R2) was 0.99 for a load-slip curve model. The study findings could offer a credible reference for the shear mechanism of such economical and environmentally friendly precast steel-UHPC composite beams in accelerated bridge construction.

Particle swarm algorithm-based identification method of optimal measurement area of coordinate measuring machine.

A particle swarm algorithm-based identification method for the optimal measurement area of large coordinate measuring machines (CMMs) is proposed in this study to realize the intelligent identification of measurement objects and optimize the measurement position and measurement space using laser tracer multi-station technology. The volumetric error distribution of the planned measurement points in the CMM measurement space is obtained using laser tracer multi-station measurement technology. The volumetric error of the specified step distance measurement points is obtained using the inverse distance weighting (IDW) interpolation algorithm. The quasi-rigid body model of the CMM is solved using the LASSO algorithm to obtain the geometric error of the measurement points in a specified step. A model of individual geometric errors is fitted with least squares. An error optimization model for the measurement points in the CMM space is established. The particle swarm optimization algorithm is employed to optimize the model, and the optimal measurement area of the CMM airspace is determined. The experimental results indicate that, when the measurement space is optimized based on the volume of the object being measured, with dimensions of (35 × 35 × 35) mm3, the optimal measurement area for the CMM, as identified by the particle swarm algorithm, lies within the range of 150mm < X < 500mm, 350mm < Y < 700mm, and -430mm < Z < -220mm. In particular, the optimal measurement area is defined as 280mm < X < 315mm, 540mm < Y < 575mm, and -400mm < Z < -365mm. Comparative experiments utilizing a high-precision standard sphere with a diameter of 19.0049mm and a sphericity of 50nm demonstrate that the identified optimal measurement area is consistent with the results obtained through the particle swarm algorithm, thereby validating the correctness of the method proposed in this study.

Accuracy improvement of laser tracker registration based on enhanced reference system points weighting self-calibration and thermal compensation.

Improving the accuracy of large measurement systems consisting of multiple laser trackers and Enhanced Reference System (ERS) points is technically challenging. In practice, standard devices with precise distance limits are often used to improve the registration accuracy of laser trackers. However, these standard devices are expensive and need to be calibrated by the Coordinate Measuring Machine (CMM). In addition, the stability of ERS points can significantly affect registration errors. Therefore, this paper proposes a laser tracker registration method based on ERS point-weighted self-calibration and thermal deformation compensation. First, a self-calibration method for simple standard devices based on multilateration measurements is presented, which only utilizes large measurement systems without additional high-precision measurement instruments. Based on this, a weighted registration optimization algorithm for the registration process of a relocation laser tracker is proposed. Then, the position errors of ERS points caused by temperature changes are calculated and compensated based on the thermal deformation coefficient of large structural components. The compensated ERS points are used for the registration of the laser trackers. Finally, the effectiveness of the proposed method is demonstrated by a field measurement experiment on a large spherical shell. Compared with the most widely used benchmark method, the proposed method reduces the average registration error of all ERS points from 0.103 to 0.02mm.

Volumetric error measurement of the five-axis machine tool using optimal measurement points based on a modified genetic algorithm

Improving the volumetric accuracy enhances the performance accuracy of the five-axis machine tool which is one of the major goals in the manufacturing industry. Several methods have been proposed for volumetric error measurement through random point selection, however, errors are sensitive to axes’ measurement points to which less attention has been given in the previous works. This work proposes a new method of selecting optimal axes’ points to measure the volumetric error of the machine tool based on the observability index of the machine tool using a modified genetic algorithm. This is to investigate the applicability and effectiveness of the observability index in selecting optimal points during error measurement. From the simulation and experimental results, the root mean square errors between the set and measured PDGEs, and the predicted and measured volumetric errors for the optimal points were lower than those of the random points. However, the RMSE between the volumetric error before and after compensation for the optimal points was higher signifying high error reduction than that of the random points. In general, the optimal points obtained higher identification accuracy results than the random points. This shows that the method of using a modified genetic algorithm based on the observability index of the machine tool to select optimal axes points during volumetric error measurement enhances the identification accuracy which will enhance the performance accuracy of the five-axis machine tool.

Positioning accuracy improvement for target point tracking of robots based on Extended Kalman Filter with an optical tracking system

Although industrial robots have been successfully used in a wide spectrum of applications for production automation, they still face challenges for many high precision tasks especially in low-volume high-mix production due to their low absolute positioning accuracy. To respond to such rapidly changing production tasks, an efficient means is required to determine the pose relationship between the robot and the workpiece without human intervention such as teaching the robot. For this purpose, the paper proposes the use of the Extended Kalman Filter (EKF) with an optical tracking system to improve the robot positioning accuracy with a particular focus on the target point tracking of the end-of-arm tool, which is an essential part for many robotic tasks. To this end, a comprehensive kinematic error model is first derived for the end-of-arm tool that accounts for the errors in the Denavit-Hartenberg (D-H) parameters, the positioning errors of the robot base and the end-of-arm tool installation. Then, by using the optical tracking system, the pose of the end-of-arm tool relative to the workpiece can be determined in an efficient way. Based on the EKF algorithm, the kinematic parameter errors of the system can be estimated online to compensate the positioning error of the target point during the robot movement. Simulation and experimental tests have been performed to demonstrate the effectiveness of the proposed method. The proposed approach utilizes the given trajectory to design a compensation scheme where the kinematic parameter errors of the robot are estimated during the motion and then the positioning error of the end-of-arm tool is compensated at the target point. As a result, this approach can improve the target point accuracy of the robot without continuous feedback to reduce the tracking error along the trajectory in real time. It is easy to implement and suitable for low-volume, high-mix scenarios to determine the pose relationship between the robot and the workpiece without human intervention.