Improvement Of Positioning Accuracy Research Articles

Trajectory error compensation method for grinding robots based on kinematic calibration and joint variable prediction

Trajectory accuracy, a crucial metric in assessing the dynamic performance of grinding robots, is influenced by the uncertain movement of the tool center point, directly impacting the surface quality of processed workpieces. This article introduces an innovative method for compensating trajectory errors. Initially, a strategy for error compensation is derived using differential kinematics theory. Subsequently, a robot kinematic calibration method utilizing ring particle swarm optimization (RPSO) is proposed to address static errors in the grinding robot. Simultaneously, a method for predicting robot joint variables based on a dual-channel feedforward neural network (DCFNN) is designed to mitigate dynamic errors. Finally, a simulation platform is developed to validate the proposed method. Simulation analysis using extensive data demonstrates an 89.3% improvement in absolute position accuracy and a 74.2% reduction in error fluctuation range, outperforming sparrow search algorithm (SSA), improved mayfly algorithm (IMA), multi-representation integrated predictive neural network (MRIPNN), etc. Algorithmic comparison reveals that kinematic calibration significantly reduces the average trajectory error, while joint variable prediction notably minimizes error fluctuation. Validation through trajectory straightness testing and a 3D printing propeller grinding experiment achieves a trajectory straightness of 0.2425 mm. Implementing this method enables achieving 86.1% surface machining allowance within tolerance, making it an optimal solution for grinding robots.

A robust SVD-UKF algorithm and its application in integrated navigation systems

To improve the positioning accuracy in Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) integrated navigation and address the issue of measurement information anomalies, an integrated navigation algorithm based on an improved Huber-M estimation with Singular Value Decomposition Unscented Kalman Filter (SVD-UKF) is proposed. During the iterative update process of the prior information matrix in the UKF filtering, the Singular Value Decomposition (SVD) is introduced. This enhancement optimizes the prior information matrix, thereby enhancing numerical stability. The tail function of the Huber-M estimation is replaced with an exponential square loss function to improve the computational accuracy of the Huber-M estimation. The improved Huber-M estimation is then incorporated into the SVD-UKF framework. This approach reconstructs the UKF prior information and measurement information. The experimental results indicate that, compared to the traditional UKF and the Sage-Husa adaptive SVD-UKF algorithm, the improved algorithm enhances velocity accuracy by 68.7% and 31.2%, respectively, and improves position accuracy by 73.5% and 38.6%, respectively. The positioning accuracy and robustness of the INS/GNSS integrated navigation system were significantly improved, particularly under conditions with abnormal measurement information. The system demonstrated enhanced stability and resistance to interference in these scenarios.

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.

Three-Dimensional Dead-Reckoning Based on Lie Theory for Overcoming Approximation Errors

This paper proposes a dead-reckoning (DR) method for vehicles using Lie theory. This approach treats the pose (position and attitude) and velocity of the vehicle as elements of the Lie group SE2(3) and follows the computations based on Lie theory. Previously employed DR methods, which have been widely used, suffer from cumulative errors over time due to inaccuracies in the calculated changes from velocity during the motion of the vehicle or small errors in modeling assumptions. Consequently, this results in significant discrepancies between the estimated and actual positions over time. However, by treating the pose and velocity of the vehicle as elements of the Lie group, the proposed method allows for accurate solutions without the errors introduced by linearization. The incremental updates for pose and velocity in the DR computation are represented in the Lie algebra. Experimental results confirm that the proposed method improves the accuracy of DR. In particular, as the motion prediction time interval of the vehicle increases, the proposed method demonstrates a more pronounced improvement in positional accuracy.

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.

Review on Importance of Satellite Based Augmentation Systems Towards Achieving Position Accuracy and Reliability

This paper reviewed the benefit of some satellite-based Augmentation systems when the needs to achieve position accuracy and reliability becomes necessary. Global Satellite Navigation System (GNSS) plays a very important roles in several areas like aviation, railway and so on to provide universal and multidimensional weather positioning and time synchronization. With significant expansion and congestion of spectrum of radio frequency, GNSS are exposed to various threats include signal interference, adverse propagation effects like multi –path and other challenges posed by satellite dynamics that can occur due antenna failure caused by environmental obstruction which may be man-made or natural. In this case, it is relevant to consider different augmentation strategies including Satellite based Augmentation System which the scope of this paper wants to cover to improve the quality of GNSS signal. Also, with advancing desire on accurate positioning, GNSS limitation can be improved using Satellite Based Augmentation System (SBAS) because it has advantage of deploying multi GNSS constellations and multi-sensor data fusion techniques which can improve position accuracy and reliability in Aviation, railway, agriculture growth and development. The paper dwells on SBAS localization, application for efficient running of railways, aviation, livestock systems. The challenges encountered toward achieving accuracy and reliability due to position error corrections are expatiated and the methods to mitigate those challenges to achieve better position accuracy and reliability are also explained.

Design and optimization of high-precision servo control system in precision positioning technology

Abstract High-precision control technology, as a general technology in the field of manufacturing and assembly, is widely used in high-end manufacturing equipment such as chip packaging equipment and high-speed CNC machining centers. In this thesis, a permanent magnet synchronous motor is used as the actuator motor of a high-precision servo control system, and vector control strategy and sliding mode observer are adopted to study and realize the servo control of permanent magnet synchronous motor based on sliding mode observer. The current, speed, and position simulation experiments of the high-precision servo control method are also carried out to compare the positioning accuracy before and after the use of the servo control method and to study the performance of the method in precision positioning. The experiments prove the correctness and effectiveness of the SMO-based PMSM high-precision servo control method and the current response speed of the servo control in this paper is fast, and the speed and position estimation errors are maintained within 1.6 r/min and 0.15 rad in steady state condition, and within 20 r/min and 20 rad in the sudden-change condition, which is able to contribute to 11.91% and 19.30% of the machine’s X-axis and Y-axis positioning accuracy improvement. The SMO-based PMSM high-precision servo control method has high steady-state accuracy and immunity to disturbances and can be used for the precision positioning of machine systems.

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.