Position Error Research Articles

Development of an external system for monitoring the couch speed in radiotherapy using continuous bed movement.

Total body irradiation before bone marrow transplantation for hematological malignancies using Radixact, a high-precision radiotherapy machine, can potentially reduce side effects and the risk of secondary malignancies. However, stable control of couch speed is critical, and direct assessment methods outlined in quality assurance guidelines are lacking. This study aims to develop a real-time couch speed verification system for the Radixact. The developed system used a linear encoder to measure couch speed directly. Accuracy was verified via a linear stage, comparing measurements with a laser distance sensor. After placing a phantom simulating the human body on the Radixact couch, the couch speed was verified using predefined speed plans. Operating the linear stage at 0.1, 0.5, and 1.0mm/s revealed that the maximum position error of the developed verification system compared to the laser distance sensor was nearly equivalent to the distance resolution of the system (0.05mm/pulse), with negligible average speed error. When the Radixact couch operated at 0.1, 0.5, and 1.0mm/s, the values obtained by the verification system agreed with the theoretical values within the sampling period (0.01s) and distance resolution (0.05mm). The verification system developed provides real-time monitoring of the speed of the Radixact table, ensuring treatment effectiveness and patient safety. It would guarantee the couch speed's soundness and contribute to the "visualization" of safety.

Fabrication of Circular Defects in 2-Dimensional Photonic Crystal Lasers with Convex Edge Structure

We have developed circular defects in 2-dimensional photonic crystal lasers that allow current injection for interconnected optical communications. However, when cleaving the sample to measure the output light, the output light intensity changes due to the cleaving position. In a previous study, we proposed a new end face structure called a convex edge structure. In this paper, we design the electron beam lithography patterns to fabricate this structure. With this design, it is possible to eliminate the effect of different cleaving positions and ensure that the cleavage tolerance is larger than the cleavage position error. We also develop the fabrication technology for this structure, fabricate samples, and measure the output light experimentally. The optical properties of the fabricated sample are similar to well-fabricated samples with normal cleavage edge faces. We are assured that these results contribute to future work such as accurate manufacturing and improving the end face configuration to obtain higher outputs.

A Bio-Inspired Sliding Mode Method for Autonomous Cooperative Formation Control of Underactuated USVs with Ocean Environment Disturbances

In this paper, a bio-inspired sliding mode control (bio-SMC) and minimal learning parameter (MLP) are proposed to achieve the cooperative formation control of underactuated unmanned surface vehicles (USVs) with external environmental disturbances and model uncertainties. Firstly, the desired trajectory of the follower USV is generated by the leader USV’s position information based on the leader–follower framework, and the problem of cooperative formation control is transformed into a trajectory tracking error stabilization problem. Besides, the USV position errors are stabilized by a backstepping approach, then the virtual longitudinal and virtual lateral velocities can be designed. To alleviate the system oscillation and reduce the computational complexity of the controller, a sliding mode control with a bio-inspired model is designed to avoid the problem of differential explosion caused by repeated derivation. A radial basis function neural network (RBFNN) is adopted for estimating and compensating for the environmental disturbances and model uncertainties, where the MLP algorithm is utilized to substitute for online weight learning in a single-parameter form. Finally, the proposed method is proved to be uniformly and ultimately bounded through the Lyapunov stability theory, and the validity of the method is also verified by simulation experiments.

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

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

Sensitivity of Cumulus Physics in ARW Model on Track of Tropical Cyclones ‘Amphan’ and ‘Bulbul’ over the Bay of Bengal

The Advanced Research Weather Research and Forecasting (ARW) model performance can be improved by analyzing the sensitivity of the physical parameterization schemes. In order to revalidate the performance of the ARW model in relation to the selection of Cumulus Physics (CP) schemes, the sensitivity of CP schemes for Tropical Cyclones (TCs) over the Bay of Bengal (BoB) region has been examined. National Centre for Environment Prediction (NCEP)’s Final Reanalysis (FNL) data (1010) have been used as lateral and initial conditions in the ARW model. The model has been configured in a single domain and runs for four different initial conditions in simulating TC ‘Amphan’ and for three different initial conditions in simulating TC ‘Bulbul’. In this study, average track errors have been found between 40-50 km for Kain-Fritsch Cumulus Potential (KFCP) and Multi-Scale Kain-Fritsch (MSKF) schemes for TC Amphan and below 60 km for the Kain Fritsch (KF) scheme for TC Bulbul. Lower landfall position error has been found below 60 km for the KF scheme for TC Amphan, and 40 km for KFCP and MSKF schemes for TC Bulbul. Journal of Engineering Science 15(1), 2024, 119-129

Dynamic environment UAV deployment algorithm based on potential game theory

AbstractTo address the issue of low coverage resulting from the challenge of acquiring the optimal deployment position in commonly used distributed deployment algorithms, this study presents a three‐dimensional deployment algorithm for Unmanned Aerial Vehicles (UAVs) based on potential games. First, a local mutually beneficial game model is designed to demonstrate the existence of exact potential games and Nash equilibrium. The Nash equilibrium solution corresponds to the maximum coverage. Next, drawing inspiration from exploration, a solution method called Exploration Spatial Adaptive Play is proposed. It utilizes the maximum utility function value from multiple step sizes in the exploration direction to update the action selection probability, thereby ensuring the optimal deployment position in each decision cycle. To address the issue of sensor position error, a method for processing sensor position errors is proposed. The simulation results demonstrate that the proposed distributed deployment algorithm achieves higher coverage compared to commonly used methods.

Indoor altitude estimation assisted by inertial compensation and online floor modeling

Abstract Indoor pedestrian altitude information plays a key role in such applications as emergency relief and military reconnaissance. However, height errors of inertial positioning grow without boundary because of the altitude channel divergence of the Strap-down Inertial Navigation System (SINS). This article proposes a new vertical location method based on the foot-mounted Inertial Measurement Unit (IMU) and online floor modeling. First, the number of stairs at each step is calculated after the height of each step is corrected by the error compensation model. Second, the prior height is estimated by utilizing the number of stairs and the stair height. Then, the key point library related to the floor is built online. Finally, the error correction of the vertical displacement is carried out by matching the prior height with the key point library. The gait classification shows that the accuracy based on the error compensation model can reach up to 99%. Moreover, the maximum attitude error is less than 1 m and the accumulated vertical positioning errors are eliminated completely when a pedestrian walks up and down inside a multi-story building, and all these have verified accuracy and robustness of vertical indoor positioning.

Automatic Robot Hand-Eye Calibration Enabled by Learning-Based 3D Vision

Hand-eye calibration, a fundamental task in vision-based robotic systems, is commonly equipped with collaborative robots, especially for robotic applications in small and medium-sized enterprises (SMEs). Most approaches to hand-eye calibration rely on external markers or human assistance. We proposed a novel methodology that addresses the hand-eye calibration problem using the robot base as a reference, eliminating the need for external calibration objects or human intervention. Using point clouds of the robot base, a transformation matrix from the coordinate frame of the camera to the robot base is established as “I=AXB.” To this end, we exploit learning-based 3D detection and registration algorithms to estimate the location and orientation of the robot base. The robustness and accuracy of the method are quantified by ground-truth-based evaluation, and the accuracy result is compared with other 3D vision-based calibration methods. To assess the feasibility of our methodology, we carried out experiments utilizing a low-cost structured light scanner across varying joint configurations and groups of experiments. The proposed hand-eye calibration method achieved a translation deviation of 0.930 mm and a rotation deviation of 0.265 degrees according to the experimental results. Additionally, the 3D reconstruction experiments demonstrated a rotation error of 0.994 degrees and a position error of 1.697 mm. Moreover, our method offers the potential to be completed in 1 second, which is the fastest compared to other 3D hand-eye calibration methods. We conduct indoor 3D reconstruction and robotic grasping experiments based on our hand-eye calibration method. Related code is released at https://github.com/leihui6/LRBO.

A novel approach on calibration of a laser line sensor’s position in the machine coordinate system

Abstract On-machine measurement facilitates real-time monitoring and feedback of the machining status of parts, thereby preventing secondary positioning errors in the offline measurement of parts. Calibrating spatial position of the measuring sensor is crucial for ensuring the accuracy of on-machine measurements. This paper introduces a novel approach to on-machine calibration of line laser displacement sensors, based on the fitted sphere-center method and iterative position in both height and width directions along the line laser. By constructing a transformation matrix and measuring multiple sets of data to determine the unknowns in the matrix, the spatial position of the laser displacement sensor can be calibrated. The results showed that the polar deviation of the standard ball radius was 0.0067 mm, confirming the feasibility and accuracy of the method.

Development of a Novel Retinal Surgery Robot Based on Spatial Variable Remote Center-of-Motion Mechanism

Abstract This article reports the design, modeling, and experiments of a novel retinal surgery robot based on spatial variable remote center-of-motion (RCM) mechanism. The general design criteria for parallel mechanisms are proposed, and the planar five-bar mechanisms are evaluated and selected. The planar-spatial evolution process, including the parallel connection of the planar mechanism and the equivalent substitution of joints, is adopted to develop a spatial variable RCM mechanism and then the robot. The mobility and singularity of the robot are analyzed, and the forward/inverse kinematics and workspace are modeled. Dimension optimization is conducted based on a comprehensive performance indicator that characterizes the motion range of linear actuators and the global dexterity performance index of robot. The prototyped robot is fabricated and assembled, and the kinematic calibration is performed. The position error of end-effector is within 34 μm, and both the position error and deviation of the RCM point are within 23 μm. The robot is demonstrated to reach the desired position and execute the RCM motion with high precision simultaneously.

A Novel Algorithm for Enhancing Terrain-Aided Navigation in Autonomous Underwater Vehicles

The position error in an inertial navigation system (INS) for autonomous underwater vehicles (AUVs) increases over time. Terrain-aided navigation can assist in correcting these INS position errors. To enhance the matching accuracy under large initial position errors, an improved terrain matching algorithm comprising terrain contour matching (TERCOM), particle swarm optimization (PSO), and iterative closest contour point (ICCP), named TERCOM-PSO-ICCP, is proposed. Initially, an enhanced TERCOM with an increased rotation angle is utilized to minimize heading errors and reduce the initial position error. The similarity extremum approach evaluates the initial matching outcomes, leading to an enhanced accuracy in the initial results. Next, artificial bee colony (ABC)-optimized PSO is employed for secondary matching to further reduce the initial position error and narrow the matching area. Finally, the ICCP, using the Mahalanobis distance as the objective function, is applied for the third matching, leveraging the ICCP’s fine search capabilities. The effective combination of these three algorithms significantly improves the terrain-aided navigation matching effect. Two tests show that the improved TERCOM-PSO-ICCP effectively reduces the matching error and corrects the position of the INS.

DIB‐BOT: An Open‐Source Hardware Approach for Plate‐Integrated Droplet Interface Bilayer Deposition

AbstractDroplet interface bilayers (DIBs) offer a controlled lipid environment for studying membrane‐bound processes, with applications in artificial cells, biosensing, and biophysics. Current DIB fabrication faces challenges due to time‐consuming processes and specialized equipment, limiting scale‐up and hindering statistical significance in single‐molecule assays. This research introduces “DIB‐BOT,” an open‐source solution combining a nanoinjector and a 3D printer. DIB‐BOT enables rapid, reproducible DIB fabrication, overcoming manual limitations. Using off‐the‐shelf components, DIB‐BOT ensures high spatial reproducibility, minimal user input, and scalable experiments. The system's utility is demonstrated through pairwise droplet assembly and a fluorescence plate‐reader assay. Compared to manual fabrication, DIB‐BOT shows a 10‐fold reduction in droplet volume error, a threefold reduction in positional error, and 100% droplet yield. This method lowers entry barriers to DIB research, expanding its applications and enhancing data quality.

Analysis of Comparability of PCV in Surveying-Grade GNSS Antenna – Topcon HIPER-VR Case Study

ABSTRACT It is well known that the phase center of a Global Navigation Satellite System (GNSS) antenna is not a stable point. For any given GNSS antenna, the phase center will change with the direction of the incoming signal from a satellite, as well as the frequency. Ignoring these phase center variations (PCVs) in GNSS data processing can lead to notable errors, especially in vertical position component determination. To avoid the problem, antenna PCV together with the phase center offset (PCO) information are recommended to be used in GNSS observation processing. We currently distinguish between individual and type-mean phase center correction (PCC) models. These models describe the variations in the phase center of the antenna as a function of the elevation angle and azimuth. In general, the primary difference between individual and type-mean models lies in their specificity. Individual models are highly precise but are valid only for a particular antenna model, while the type-mean models are more general and can be applied to a broad range of antennas of the same type, but may suffer from a lower level of precision. This paper aims to analyze the comparability of PCV in surveying-grade GNSS antennas. For the analyses, we propose to use an originally designed bench with precisely defined relative positions of the seven antenna mounting points. Preliminary studies have been performed using GPS observations on L1 and L2 frequencies recorded by seven Topcon HIPER-VR antennas. The results proved that the comparability of PCV for this antenna is high. The position error did not exceed 3 mm. It could be assumed that the type-mean PCC model could describe PCV all antennas of this type with good accuracy.

Inverse kinematics of six degrees of freedom robot manipulator based on improved dung beetle optimizer algorithm

Inverse kinematics is a basic problem in robotics, which aims to solve the robot’s joint angles according to the end effector’s position and orientation. This paper proposed an improved spiral search multi-strategy dung beetle optimizer (DBO) algorithm for solving the inverse kinematics problem. The improved DBO algorithm considers not only the error between the target value and the current value but also the previous position of the robot to ensure minimum displacement during the movement. To solve the end position error and orientation error of the robot end effector more accurately, the quaternion is introduced as a penalty factor in the optimization objective function, which is of great significance for reducing the orientation error. Through the improved DBO algorithm, the position error is still accurate, and the orientation error is reduced from 9.5901 to 1.8718. Experimental results show that the proposed algorithm outperforms other swarm-intelligent algorithms in terms of accuracy and convergence speed. Overall, the proposed spiral search multi-strategy DBO algorithm provides an effective and efficient solution to the inverse kinematics problem in robotics.

A six degrees of freedom femtosecond laser system for fabrication of small-scale mechanical property specimens.

We present the details of a novel ultra-short pulsed laser machining workstation that has been employed for high-throughput laser machining of small-scale mechanical property specimens. This system employs a six degrees of freedom hexapod positioning stage capable of macroscopic movements at high positional accuracy. We developed a methodology that uses quantitative image analysis to measure key parameters required to minimize the hexapod positioning and rotational error. Application of this system to laser machining of small-scale 316L stainless steel tensile specimens and ultra-high molecular weight polyethylene compressive specimens using eucentric tilt and rotation about the specimen axis will be shown, where serial laser milling at a specimen tilt angle of 10° was used to effectively eliminate any taper in the sample cross section that is typically found in laser machining.

6-Degree Vision based Tracking of a Mandible Phantom with Deep Learning

Abstract During maxillofacial surgery, the precise placement of surgical tools is crucial for accurate implant placement. Particularly if multiple implants are needed, e.g., after cancerous bone removal, visual landmarks might not be obtainable. To this end, we propose a vision-based tracking approach with deep learning. Our markerless tracking approach is based on video streams from two cameras for tracking a mandible phantom. We study, to what extent vision-based localization using deep learning is feasible. For real-time 6D pose estimation we propose a Siamese network with a ResNet-18 subnetwork. We acquire a large training dataset with a robot and evaluate the tracking accuracy on partially occluded images. Thereby, we mimic visual information that is accessible during clinical interventions. We report a mean position error of 1.33 ± 1.14mm and a rotation error of 0.86 ± 0.71 deg for partially occluded images. Overall we present a promising tracking approach that is marker-free and robust toward image artifacts.

Research on positioning accuracy of the swivel head of milling and turning complex machining center

Abstract This article focuses on the swivel head of a milling-turning complex machining center and establishes a geometric error model between the rotary axes based on the multi-body system theory and the homogeneous coordinate transformation method. The double ball bar (DBB) is used to identify the geometric errors of the machine tool swivel head position-independent by measuring circular trajectories. The impact of PIGEs on the measurement trajectory is simulated. The swivel head is rotated at four angles (B0°, B45°, B90°, B-15°), and the laser tracker is used to analyze and study the linear axis straightness, perpendicularity, and the center coordinates and plane errors of the swivel head movement along the XZ plane. A swivel head rotation accuracy test fixture is designed to correct the positioning accuracy of a swivel head with a 45° inclination of the rotation axis. The research results show that the fluctuation of the center plane degree of freedom of the swivel head moving along the XZ plane is small, and the maximum value is 0.0086mm. Under the 45° angle posture of the swivel head, both the center coordinate error value and the plane error value are the largest. The simulation results of the swivel head position error are fairly consistent with the center coordinate error of the swivel head plane movement. A backward tilt displacement of 0.01mm~0.015mm of the machine column during assembly can reduce the influence of the weight of the swivel head on the perpendicularity and straightness of the vertical reciprocating motion, and adding auxiliary support near the base of the swivel head zero position can reduce machine straightness error.

Enhanced rainfall nowcasting of tropical cyclone by an interpretable deep learning model and its application in real-time flood forecasting

Reliable Tropical Cyclone (TC) rainfall and flood forecasts play an important role in disaster prevention and mitigation. Numerous studies have demonstrated the promising performance of deep learning in hydrometeorological forecasts. However, few studies have investigated the potential enhancement of advanced TC track forecasts in predicting rainfall and induced flood. In this study, a novel rainfall nowcasting model (TCRainNet) is developed by fusing TC track characteristics with antecedent rainfall in a Convolution LSTM to predict hourly rainfall with a lead time of 6 h. The nowcasts are subsequently used to drive an event-based Xin’anjiang hydrological model for real-time flood forecasting. The model performance is interpretated by the occlusion sensitivity approach, and the propagation of errors from TC track forecasts to flood forecasts is quantified. The results underscore the superiority of TC track characteristics as input features for rainfall nowcasts, as indicated by a Mutual Information value of up to 0.51. The generated nowcasts are found to have averaged Probability of Detection (POD) and Critical Success Index (CSI) greater than 0.27 and 0.2 respectively. The Mean Absolute Error (MAE) of the nowcasts falls below 2.6 mm, which is only 46 % of the ECMWF operational high-resolution forecasts. The rainfall-driven flood forecasts have NSE greater than 0.7 and PBIAS smaller than 20 % with lead time up to + 4 h. It is shown that the position error of 0.45° and intensity error of 10 hPa&7.8 m/s in TC track forecasts generally result in 0.9 mm degradation in rainfall forecasts and 10% decline in the accuracy of rainfall-driven flood forecasts. The effectiveness of our method presents favorable applicability in advancing disaster mitigation efforts.

Analysis and Suppression of Static Electromagnetic Ripple Torque in Integrated On-Board Charging System With Rotor Position Error

Analysis and Suppression of Static Electromagnetic Ripple Torque in Integrated On-Board Charging System With Rotor Position Error

The Loran-C Pseudorange Positioning and Timing Algorithm Based on the Vincenty Formula

To improve the positioning accuracy of the Loran system and meet the requirements of Loran/BDS integrated positioning and timing, it is necessary to enhance the traditional Loran hyperbolic positioning method, making its pseudorange calculation consistent with the BDS positioning and timing solution. The existing pseudorange algorithm based on the Andoyer-Lambert formula has issues such as strict initial value selection range and susceptibility to singularities during calculations. This study proposes a new Loran pseudorange calculation method based on the Vincenty distance formula and conducts a simulation analysis of it. The results show that, in the absence of noise interference, the positioning and timing errors of this pseudorange algorithm are close to zero, demonstrating high accuracy. When subjected to random noise with a standard deviation of less than 100 ns, the latitude and longitude errors are both less than 10 m, and the timing error is less than 10−4 ns, meeting the requirements of Loran positioning and timing. Compared to the pseudorange algorithm based on the Andoyer-Lambert formula, the one based on the Vincenty formula has comparable positioning longitude accuracy but superior timing accuracy. Moreover, the latter offers a wider range of initial value selection and can avoid more singularity issues during calculations.