Improvement Of Positioning Accuracy Research Articles

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.

Kinematic and Joint Compliance Modeling Method to Improve Position Accuracy of a Robotic Vision System.

In the field of robotic automation, achieving high position accuracy in robotic vision systems (RVSs) is a pivotal challenge that directly impacts the efficiency and effectiveness of industrial applications. This study introduces a comprehensive modeling approach that integrates kinematic and joint compliance factors to significantly enhance the position accuracy of a system. In the first place, we develop a unified kinematic model that effectively reduces the complexity and error accumulation associated with the calibration of robotic systems. At the heart of our approach is the formulation of a joint compliance model that meticulously accounts for the intricacies of the joint connector, the external load, and the self-weight of robotic links. By employing a novel 3D rotary laser sensor for precise error measurement and model calibration, our method offers a streamlined and efficient solution for the accurate integration of vision systems into robotic operations. The efficacy of our proposed models is validated through experiments conducted on a FANUC LR Mate 200iD robot, showcasing notable improvements in the position accuracy of robotic vision system. Our findings contribute a framework for the calibration and error compensation of RVS, holding significant potential for advancements in automated tasks requiring high precision.

Implementation of GAGAN augmentation on smart mobile devices and development of a cooperative positioning architecture

Abstract This study presents an Android-based cooperative positioning (CP) architecture to improve the GNSS positioning performance on mobile devices. SBAS (Satellite Based Augmentation System) augmentation increases positioning accuracies significantly by sharing corrections between SBAS-enabled and non-capable devices via wireless connection or using a central server. The Indian GAGAN (GPS Aided GEO Augmented Navigation) is employed and assessed in the experiments. If GAGAN corrections are applied, all three chosen mobile devices showed a positioning accuracy improvement of around 95 %. The average 2D RSME was reduced from 75.23 to 1.35 m for the single-frequency GNSS smartphone Xiaomi Redmi Note 8 and from 33.25 to 1.62 m for the dual-frequency Google Pixel 4. As expected, the third GIS mapping device, Stonex S70 tablet, showed the highest performance, achieving sub-meter positioning accuracies. Thus, the experiment has proven the suitability of GAGAN augmentation for mobile devices, providing positive insight for further development of the CP architecture.

End Positioning Accuracy Improvement for Soft Arms Under Various Loads

End Positioning Accuracy Improvement for Soft Arms Under Various Loads

Positioning accuracy prediction of precision cycloid reducer based on a BDDTE model and use in design optimization

As users' expectations for precision cycloid reducer positioning accuracy rise, accurate positioning accuracy prediction of products before assembly and design solutions to improve product qualification rates are very important. Considering the actual manufacturing conditions of components, a new multi-source error (MSE) equivalence modeling assumption and pin gear's MSE measurement method are proposed, and the rationality of the assumption is verified through experiments. Based on the geometric characteristics of each component's MSEs, a new analytical model of bi-directional drive transmission error (BDDTE) is established, which organically unifies the prediction of transmission error (TE) and lost motion (LM). The influence of the output mechanism on the positioning accuracy of the entire reducer is found to be non-negligible through Monte Carlo simulation; meanwhile, the general tolerance allocation scheme makes it difficult to ensure that each prototype meets the performance requirements. For this reason, a new design solution for the positioning accuracy of the reducer with the pin gear and output mechanism as the optimization targets is proposed. Finally, the prototype test results show that the BDDTE model based on the new assumption has a higher accuracy in predicting positioning accuracy compared with the model based on the conventional assumptions. Meanwhile, the effectiveness of the proposed improvement design solution for positioning accuracy improvement is verified. The BDDTE model reveals the intrinsic connection between the TE and LM of the cycloid reducer, and the improvement solution has guiding effects on improving the product qualification rate.

Geometric Positioning Accuracy Improvement Method of Micro SAR Satellites with Large Positioning Errors

Geometric Positioning Accuracy Improvement Method of Micro SAR Satellites with Large Positioning Errors

A Novel Positioning Accuracy Improvement Method for Polishing Robot Based on Levenberg–Marquardt and Opposition-based Learning Squirrel Search Algorithm

A Novel Positioning Accuracy Improvement Method for Polishing Robot Based on Levenberg–Marquardt and Opposition-based Learning Squirrel Search Algorithm

Adverse effects of receiver clock bias and drift on NavIC user position coordinates and their mitigation

We investigated the effects of receiver clock bias and drift on NavIC receiver position coordinates at a low latitude station and proposed a mitigation technique. For this, Five weeks data of Accord IRNSS-GPS-SPS (AIGS) receiver are considered. Error in each position coordinates (X, Y and Z) is estimated and observed that the maximum error occurred in user position Z-coordinate. CEP for 2D position is analyzed and observed that the Circular Error Probability (CEP) values are high in Z coordinate of YZ and ZX planes. Further, it is noticed that the timing of sudden enhancement of both clock bias and drift parameters coincides with the periodic anomaly of the X, Y and Z coordinates. The error in individual position coordinates is minimized by applying 6th order Butterworth low pass filter resulting in improved position accuracy. If such errors are not mitigated, the position estimation of the receiver becomes highly erroneous.

Implementing Real-Time DOV Compensation: A Practical Approach Using a MLP on an NPU Embedded in an INS

This paper explores the impact of gravity disturbances on INS accuracy and presents a method for real-time compensation during the navigation process. By utilizing data from the precise gravity model, EGM2008, a novel approach to compensate for the DOV in real time on the INS’s built-in NPU was introduced. This method predicts gravity disturbances while traveling for platforms on both land and water, utilizing the MLP technique. To predict these gravity disturbances, four distinct MLP models, MLP1~MLP4, were designed and their supervised learning results were compared using HMSE and RMSE. This comparative analysis allowed us to identify that the MLP4 model exhibited the best performance. In order to validate the proposed method, MLP4 was implemented inside the NPU and the measured execution time was 1.041 ms. The field test was conducted with real-time execution of the MLP4 model on the NPU of the INS. The results of this field test clearly demonstrated the effectiveness of the proposed approach in enhancing position accuracy. Over the course of a 2 h field test, it was evident that employing the proposed method improved position accuracy by a notable 27%.

V-RTK: A velocity-constrained RTK algorithm to improve position accuracy of low-cost receiver in urban environments

In harsh signal environments, such as urban canyon, overpass and so on, global navigation satellite system (GNSS) navigation will be influenced by insufficient satellites, signal degradation, and bad quality which lead to the lack of redundancy and re-initialization of the ambiguities, especially for the low cost real-time kinematic (RTK) equipment. To address these issues, an velocity-constrained RTK method (V-RTK) for low-cost receiver is proposed. Instead of being initialized with the standard point positioning (SPP) result, the predicted position parameters are calculated with the velocity obtained from GNSS observation or other sensors, so that the historical filter message can be delivered to the next epoch both from the ambiguity domain and the position domain to maintain the accuracy of historical epochs. The performance of the proposed method has been assessed with two kinematic vehicular datasets collected in an urban environment, which lasts about 3 h. The results manifest that the positioning trajectory is smoother and location jumps are significantly reduced after applying the velocity constraint. And the accuracy of each dimension is improved by 10%∼40%. Besides, an in-field experiment is conducted to compare the proposed method with the PVA method, which shows a better performance in our proposed method for making full use of observations.

Experimental Research of Triple Inertial Navigation System Shearer Positioning.

In order to improve the positioning accuracy of shearers, the overground experimental device based on the positioning model of TINS (Triple Inertial Navigation System) was built. The influence of TINS installation parameters on positioning accuracy was discussed through two sets of experiments: the inter-INS (Inertial Navigation System) distances influence experiments and the tri-INS plane spatial position influence experiments. The results show that the positioning accuracy of the shearer is improved to a different extent under the two sets of experimental conditions. When the inter-INS distances are 0.2 m, the positioning accuracy is the highest and the positioning accuracy improvement effect is also the best. When the negative plane α3 is 45°, the positioning accuracy is the highest, and the positioning accuracy improvement effect is also the best. The analysis shows that the main factor affecting the positioning accuracy is the precision of the evaluated values outputs of TINS from EKF (Extended Kalman Filter). Considering the positioning accuracy, equipment installation convenience and so on, the optimum installation parameters are 90° (horizontal installation) α3 for the positive plane and 0.2 m inter-INS distances.

Two-stage clustering for improve indoor positioning accuracy

High accurate positioning, as a key factor for the most resource management algorithms, has attracted a lot of attention due to its crucial role in some new applications such as smart factories, IoT, and autonomous cars. Indoor positioning, as another major category of positioning in addition to outdoor positioning, is a very complicated process and achieving high accuracy is a tough process. This paper proposes a two-stage clustering-based approach (TSCA) for indoor positioning to deal with highly complex data sets which was collected from a large-scale indoor radio system. In the first stage, a new clustering algorithm, called group matching method is developed, which divides the dataset into several groups (sub-datasets) according to the different reference signal received power (RSRP) values of the real-world dataset. In the second stage, KNN and its variants, are used to match and evaluate the location of each device in one of sub-datasets instead of the entire dataset, which can increase the accuracy of the positioning. This method can perfectly solve the problem of uneven distribution of reference point data in the process of data acquisition, which is a popular challenge for most real scene data acquisition. The proposed method is compared with several state-of-the-art ML methods such as Support Vector Regression (SVR), and clustering methods such as K-means. The results indicate a high positioning accuracy improvement of more than 55% compared to a modified KNN method use our own RSRP fingerprint dataset, and a 2D accuracy improvement of 13.36% and a 3D accuracy improvement of 10.3% compared to a traditional KNN method use a Wi-Fi Received Signal Strength fingerprint.

A Novel Device-Free Positioning Method Based on Wi-Fi CSI with NLOS Detection and Bayes Classification

Device-free wireless localization based on Wi-Fi channel state information (CSI) is an emerging technique that could estimate users’ indoor locations without invading their privacy or requiring special equipment. It deduces the position of a person by analyzing the influence on the CSI of Wi-Fi signals. When pedestrians block the signals between the transceivers, the non-line-of-sight (NLOS) transmission occurs. It should be noted that NLOS has been a significant factor restricting the device-free positioning accuracy due to signal reduction and abnormalities during multipath propagation. For this problem, we analyzed the NLOS effect in an indoor environment and found that the position error in the LOS condition is different from the NLOS condition. Then, two empirical models, namely, a CSI passive positioning model and a CSI NLOS/LOS detection model, have been derived empirically with extensive study, which can obtain better robustness identified results in the case of NLOS and LOS conditions. An algorithm called SVM-NB (Support Vector Machine-Naive Bayes) is proposed to integrate the SVM NLOS detection model with the Naive Bayes fingerprint method to narrow the matching area and improve position accuracy. The NLOS identification precision is better than 97%. The proposed method achieves localization accuracy of 0.82 and 0.73 m in laboratory and corridor scenes, respectively. Compared to the Bayes method, our tests showed that the positioning accuracy of the NLOS condition is improved by 28.7% and that of the LOS condition by 26.2%.

Sensitivity analysis of a double-parallelogram based RCM mechanism used for MIS robots

Parallelogram-based remote center-of-motion (RCM) mechanisms are extensively used to design robots for minimally invasive surgery (MIS). For their kinematic modeling, classically, it is assumed that link parameters of the opposite sides of constituting parallelograms are equal which made the RCM an invariant point and simplified the prescribed modeling process. However, link parameter equality does not exist in actual practice due to the inevitable constraints of manufacturing, assembly and testing/measurement, etc. Consequently, the RCM and surgical end-effector deviate from their theoretically designed positions. This paper introduces a novel approach to model the kinematics of parallelogram-based mechanisms by accounting for the error in manufactured link parameters. Additionally, it works out the types, possible ways, order, and nature of the said deviation. Further, a double-parallelogram-based RCM mechanism is adopted to demonstrate the proposed concept, and a prototype of the same is manufactured using laser micromachining and rapid prototyping techniques. Thereafter, the developed kinematic models are validated both analytically and physically. In contrast to the results given by the conventional kinematic modeling approach, the results obtained through the proposed approach and performed experiment showed better agreement with each other. The sensitivity of RCM and surgical end-effector to the inequalities, involved in the manufactured prototype, is revealed through several plots in the Matlab environment. A significant improvement in positional accuracy of the RCM and surgical end-effector have been achieved. It proves the efficacy of the presented method and it is believed that it applies to other parallelogram-based mechanisms as well.

Carrier Characteristic Bias Estimation between GNSS Signals and Its Calibration in High-Precision Joint Positioning

A distinctive feature of modern Global Navigation Satellite System (GNSS) signals is that they transmit multiple signal components at the same carrier frequencies. The idea of joints across the signal channels from the same carrier frequency and even across different frequencies has been presented in many studies for tracking purposes. Carrier joint tracking is required on the premise that the frequency and phase relationship between signals are nominal values, and the bias of carrier characteristics between signals is drowned in noise as the signal reaches the ground, which requires high-gain receiving equipment to restore the original signal. The space signal-quality monitoring and evaluation system built by the National Timing Center of the Chinese Academy of Sciences is based on a 40 m dish antenna, which can automatically track a single satellite and achieve a high-fidelity reception of navigation signals to a certain extent, realizing fine signal quality monitoring (SQM) of GNSS satellites. Based on this platform, we discuss four types of time distributions of the combined signals among different signal components and provide a method to estimate the carrier characteristic bias between GNSS signals. We derived the correction method of carrier characteristic bias in the joint reception by the joint tracking mathematical model. Under the conditions of narrow correlation and unobstructed case, the carrier characteristic deviation does not vary significantly with the correlator interval and the satellite elevation angle. Based on the results of stability analysis, it is recommended that the receivers should update the carrier frequency bias correction number of the intra-frequency signal and carrier phase bias correction number of the intra-frequency signal monthly. The carrier phase deviation correction number of the inter-frequency signal is performed daily. The measured data from satellites show that the phase accumulation error of the joint tracking carrier loop can be eliminated to achieve long-term stable tracking after frequency bias correction. After the carrier phase bias correction, the joint positioning accuracy of the B2a and B2b signals was improved by 0.81%, and those of the B1C, L1C, E1C, and B2a signals were improved by 0.35%, 0.04%, 0.20%, and 0.11%, respectively. The positioning accuracy improvement effect of inter-frequency signals was greater than that of intra-frequency signals after carrier phase correction.

Developing an image processing pipeline to improve the position accuracy of single UAV images

Unmanned aerial vehicle (UAV) based remote sensing has been extensively used in precision agriculture applications, such as vegetation growth and health monitoring, yield estimation, and irrigation management. Conventional procedures for UAV data collection and processing require collecting highly overlapped images, stitching images to generate an orthomosaic, and using ground control points (GCPs) in the field or UAV onboard real-time-kinematic (RTK) global navigation satellite system (GNSS) data to improve position accuracy. For improving efficiency, a previous study developed a framework to process individual UAV images for mapping cotton emergence. The current study aimed to build a near-real time image processing pipeline to further improve the positioning accuracy of single UAV images. The improved image processing pipeline comprised feature detection and matching, false matches removal, geometric transformation matrix calculation, crop row alignment, image position assignment, and mapping. The developed pipeline was tested for mapping in both cotton and corn fields. Results showed that the position accuracies for measuring the distance between GCPs were 0.32 ± 0.21 m and 0.57 ± 0.28 m in a cotton and a corn field, respectively, when compared to ground truth data collected with an RTK-GNSS. The developed pipeline did not require GCPs in the field or image post-processing steps, such as image mosaicking and feature extraction, which allowed processing in near-real time and may possibly be implemented in real-time using an onboard edge computing system. The pipeline was used to map emergence parameters for cotton and corn fields, including stand count, canopy area, mean days to imaging after emergence, and plant spacing standard deviation. These maps demonstrated the success of the developed methods in providing a low-cost near real-time tool (8.6 and 3.6 s/image for the cotton and corn fields, respectively) for mapping emergence parameters at field-scale for use in both research and agricultural production.

A Vehicle-Carried INS Positioning Accuracy Improvement Method by Using Lateral Constraint in GPS-Denied Environment

When the vehicle passes through the tunnel or bridge, Global Positioning System (GPS) will fail due to signal occlusion or interference. In order to improve the positioning accuracy of vehicle-carried inertial navigation system (INS) in GPS rejection environment, the lateral constraint method based on vehicle-carried INS is studied in this article. According to the characteristics of lateral constraint, in the area with good GPS signal, installation errors between the INS body coordinate frame and the vehicle coordinate frame are estimated at the same time of integrated navigation. In the GPS-denied area, the estimated installation errors are compensated to calculate the reference velocity at first. After compensation, the difference between INS velocity and reference velocity is taken as the measurement. Finally, the Kalman filter model is established to correct the inertial navigation position. In addition, the simulation and experimental verification of the proposed lateral constraint method are carried out. The results show that the INS positioning accuracy can be improved above 50% by using the lateral constraint method in GPS-denied environment, which verifies the effectiveness of the proposed lateral constraint method.

Low Cost GNSS Trimble BD982 and U-blox Performance Test Analysis F9 Series for Several Measurement Methods (Case Study: Sidoarjo Regency)

Global Navigation Satellite System (GNSS) is a tool that can assist community activities, especially in navigation and positioning for mapping surveys. This tool has an accuracy of up to millimeters for determining the position of latitude and longitude, but to get this accuracy requires an expensive cost, which is more than 200 million rupiah. Currently the development of GNSS receivers is very rapid, one of which is a low-cost GNSS product. The advantages of this receiver are that it has a low cost, light weight and an accuracy of up to centimeters for determining latitude and longitude positions. In this study, we have conducted trials using a low-cost GNSS receiver from several products such as U-blox, Trimble, and Comnav. For products from U-blox in this study, the F9P and F9R series were used. The F9R series is one of the receivers that has been integrated with the inertial measurement unit (IMU)/inertial navigation system (INS), the benefit of this integration is to improve position accuracy when data collection using the kinematic method. Products from U-blox have position accuracy below 1 meter. Meanwhile, the series used in Trimble and Comnav products are Trimble BD982 and Comnav Oem Board K708 which have accuracy below 1 centimeter. The purpose of this activity is to compare all these receivers by using Comnav OEM Board K708 data as validation data. Several parameters in this study will be analyzed such as the number of satellites obtained by the receiver, position accuracy and various other analyzes. The measurement methods that will be carried out in this study are static differential and Real Time Kinematic (RTK) measurements that utilize direct corrections from Continuously Operating Reference Stations (CORS) and deformation observations. The results of the first analysis obtained in this activity are the number of satellites obtained by the U-blox F9 series receiver which is more stable to receive satellites, namely as many as 24-29 satellites, then Trimble BD982 as many as 2-32 satellites, and Comnav K708 receivers as many as 3-28 satellites. Meanwhile, the Dilution Of Precision (DOP) values for 4 receivers, namely K708, F9P, and F9R, have stable values ranging from 0.5-1 compared to the BD982 receiver which fluctuated in several observation epochs, especially in the last observation epoch. In observations using the static differential method, using corrections from CORS for positioning this receiver can be used because the difference in position data is in the range of 0.009 m - 0.040 m from reference data (receiver K708). Meanwhile, the data receiver that has been integrated with IMU/INS (F9R) kinematic method has the smallest position difference value (K708) compared to other receivers, which is from 0.001 m - 8.091 m. In testing for deformation measurements, further research needs to be done because the data obtained is still in the form of a simulation.

Improving the performance of GNSS precise point positioning by developed robust adaptive Kalman filter

Global Navigation Satellite Systems (GNSS) Precise Point Positioning (PPP) is a great precision positioning method based on GNSS. PPP based on multi-constellation GNSS uses the extended Kalman filter (EKF). Inappropriately, the measurement outliers and the system's dynamic model might produce mistakes in the positioning accuracy acquired using this method. An adaptive robust Kalman filter (RKF) was recently developed in order to alleviate these errors. In addition, the preceding variance is sometimes used to determine the weights of different categories of observations, which has an effect on PPP performance. A novel developed robust adaptive filtering (DRKF) has been developed to provide better placement results. For the equivalent weight matrix to be generated, an equivalent weight model that is statistically robust by the chi-square test for classification is constructed in this technique. According to prior Kalman filter models employing International GNSS Service (IGS) final clock and orbit products, the performance of GNSS PPP using the DRKF is confirmed in contrast to those produced using the current model. DRKF surpasses traditional robust adaptive filters in terms of positioning accuracy, convergence time, and PPP sturdiness according to the results of this study. In comparison to RKF, DRKF improves the positioning solution by 201 mm with 119.10 %, 77 mm with 164.86 %, and 160 mm with 211.74 % in the east, north, and up (ENZ) directions, respectively. It is recommended to use the newly developed robust adaptive Kalman filter in kinematic mode, especially in urban areas, due to the noticeable improvement in position accuracy.

A Robust Nonlinear Filter Strategy Based on Maximum Correntropy Criterion for Multi-GNSS and Dual-Frequency RTK

The multi-constellation, multi-frequency Global Navigation Satellite System (GNSS) has the potential to empower precise real-time kinematics (RTK) with higher accuracy, availability, continuity, and integrity. However, to enhance the robustness of the nonlinear filter, both the measurement quality and efficiency of parameter estimation need consideration, especially for GNSS challenging or denied environments where outliers and non-Gaussian noise exist. This study proposes a nonlinear Kalman filter with adaptive kernel bandwidth (KBW) based on the maximum correntropy criterion (AMC-KF). The proposed method excavates data features of higher order moments to enhance the robustness against noise. With the wide-lane and ionosphere-free combination, a dual frequency (DF) data-aided ambiguity resolution (AR) method is also derived to improve the measurement quality. The filtering strategy based on the DF data-aided AR method and AMC-KF is applied for multi-GNSS and DF RTK. To evaluate the proposed method, the short baseline test, long baseline test, and triangle network closure test are conducted with DF data from GPS and Galileo. For the short baseline test, the proposed filter strategy could improve the positioning accuracy by more than 30% on E and N components, and 60% on U. The superiority of the proposed adaptive KBW is validated both in efficiency and accuracy. The triangle network closure test shows that the proposed DF data-aided AR method could achieve a success rate of more than 93%. For the long baseline test, the integration of the above methods gains more than 40% positioning accuracy improvement on ENU components. This study shows that the proposed nonlinear strategy could enhance both robustness and accuracy without the assistance of external sensors and is applicable for multi-GNSS and dual-frequency RTK.