Real-time Position Research Articles

Research on unmanned vehicle obstacle avoidance technology based on LIDAR and depth camera fusion

Abstract To address the problems of poor accuracy of traditional EKF algorithm in estimating the position of unmanned vehicles and the deficiencies in accuracy and map completeness of the traditional map building method with single-line LiDAR, this paper proposes a method to create fused raster maps realized with multi-source data. Firstly, the combined data of the inertial measurement unit and wheel encoder are corrected by adding the positional information output from the visual odometer using the error-state SLAM algorithm, and the local raster constructed by LiDAR and depth camera is fused frame by frame using the idea of Bayesian estimation to finally generate the fused global map. Then, a four-wheeled mobile unmanned vehicle with a LiDAR sensor and depth camera is selected as the experimental object, and dynamic environment avoidance simulation experiments are conducted to draw conclusions. The simulation experiment results show that when γ = 5.99, the algorithm generates a new local target point p g 2 (17.49, 13.49) and the corresponding getaway path and finally guides the unmanned vehicle to the specified target point, verifying that the method in this paper can achieve the avoidance capability of the unmanned vehicle in the process of getting away from the newly emerged obstacles. This study uses the scanned data of LiDAR for the estimation of the real-time position of the unmanned vehicle to realize obstacle avoidance and path planning of the unmanned vehicle.

Attitude measurement of permanent magnet spherical motors based on adaptive mahony complementary filtering

An adaptive attitude measurement algorithm called adaptive mahony complementary filter(AMCF) is proposed to address the issues of drift and noise in gyroscope measurements, as well as the high computational complexity, error accumulation, and sensitivity to noise in the quaternion extended kalman filter(QEKF) within the permanent magnet spherical motor(PMSM) inertial measurement unit. Firstly, this method corrects the gyroscope’s angular velocity bias by utilizing the error between theoretical and actual accelerations. Subsequently, the covariance is adjusted by comparing dynamic accelerations to mitigate disturbances. The fourth-order Runge-Kutta method is employed to continuously update the quaternion, thereby achieving higher accuracy in attitude measurement. Finally, static and dynamic experimental investigations are conducted on the PMSM. The experimental results demonstrate that the AMCF offers faster computation speed compared to the QEKF and higher accuracy than the fast complementary filtering(FCF). This indicates that utilizing the AMCF enables better real-time attitude measurement for three-degree-of-freedom mechanical structures.

Flexible Ultrasound Transducer With Embedded Optical Shape Sensing Fiber for Biomedical Imaging Applications.

Flexible ultrasound transducers (FUTs), capable of conforming to irregular surfaces, have become a research hotspot in the field of medical imaging. With these transducers, high-quality ultrasound images can be obtained only if strict design criteria are fulfilled. Moreover, the relative positions of array elements must be determined, which are important for ultrasound beamforming and image reconstruction. These two major characteristics present great challenges to the design and fabrication of FUTs compared to that for traditional rigid probes. In this study, an optical shape-sensing fiber was embedded into a 128-element flexible linear array transducer to acquire the real-time relative positions of array elements to produce high-quality ultrasound images. Minimum concave and convex bend diameters of approximately 20 and 25 mm, respectively, were achieved. The transducer was flexed 2000 times, and yet no obvious damage was observed. Stable electrical and acoustic responses confirmed its mechanical integrity. The developed FUT exhibited an average center frequency of 6.35 MHz, and average -6-dB bandwidth of 69.2%. The array profile and element positions measured by the optic shape-sensing system were instantly transferred to the imaging system. Phantom experiments for both spatial resolution and contrast-to-noise ratio proved that FUTs can maintain satisfactory imaging capability despite bending to sophisticated geometries. Finally, color Doppler images and Doppler spectra of the peripheral arteries of healthy volunteers were obtained in real time.

A Circularly-polarized Patch Antenna for BDS Applications

The BDS (Beidou) Navigation Satellite System has been a powerful tool in real-time navigation and positioning. In the transitional stage between BDS-2 and BDS-3, it would be a top priority to ensure the antenna is compatible with the two systems. In our investigations, a circularly-polarized (CP) patch antenna is proposed for receiving B1I signals. This antenna is a newly designed slit-loaded square patch structure. The T-shape structure with F-shape slots is deployed on each edge of the antenna, and a single feed is employed to excite right-hand CP radiation. In simulations, all the parameters of the antenna are optimized through Ansys HFSS. In experiments, the processed antenna is further testified, where the measured bandwidth is compared with the simulated one to verify the effectiveness of the antenna.

Differential Time of Arrival Based Opportunistic Positioning for Asynchronous Cellular Signals: Model, Algorithms and Experiments

Signal asynchronism significantly challenges opportunistic cellular signal positioning. Existing solutions typically rely on global navigation satellite system (GNSS)-assisted initialization or multiterminal collaboration, resulting in limited application scenarios and high implementation costs. To overcome this, a differential time-of-arrival (DTOA)-based asynchronous cellular signal positioning system is developed. By treating the user equipment at the previous time as a differential reference, a DTOA positioning model is presented to implement asynchronous cellular positioning completely independently. The Taylor-series method, which has Cramer–Rao lower bound accuracy, is considered to solve the DTOA equations. To ensure global convergence, the two-step weighted least squares (TSWLS) method and its bias reduction methods are extended to DTOA positioning to obtain an initial position guess. The linear equations at each step are reconstructed, and TSWLS ambiguity is eliminated. Simulations are conducted to analyze the DTOA positioning geometry and the performance of the proposed algorithms. The proposed system implements preliminary positioning based on DTOA and exploits an extended Kalman filter for real-time positioning. It is validated experimentally, the DTOA and EKF positioning root mean-squared errors in the field test are 4.0 m and 4.2 m, respectively, which are close to the accuracy of a conventional system with GNSS-assisted initialization.

Real-time Detection System for Portable Transient Electromagnetic Unexploded Ordnance

Portable transient electromagnetic systems are not limited by terrain and can detect unexploded ordnance very flexibly. However, existing data processing methods are slow and cannot achieve real-time positioning of targets. In this paper, based on a laboratory-made portable transient electromagnetic detection system, the magnetic dipole model and tensor positioning algorithm are used to achieve fast and accurate target positioning. Using the magnetic gradient tensor algorithm for positioning reduces the calculation load, allowing it to run on embedded systems, and improving target positioning speed while ensuring the accuracy of measurements. Experimental results show that the vertical and horizontal positioning errors of the UXO are within 10 cm, and single-point positioning can be completed within 10 ms.

Real-Time Kinematic Positioning (RTK) for Monitoring of Barchan Dune Migration in the Sanlongsha Dune Field, the Northern Kumtagh Sand Sea, China

Dune migration is one of the main processes in arid lands’ geomorphology and is important for the design of windbreaks and sand fixation projects and for the monitoring of desertification dynamics. We conducted long-term continuous positioning monitoring of barchan dunes using RTK equipment and wind regime monitoring in the Sanlongsha dune field, which is located in the northern part of China’s Kumtagh Desert. We analyzed the wind energy environment of the study area, the migration characteristics of different positions in the barchan dune, and dune shape changes during different periods. We found that (1) comparing the differences in migration distance and direction measured at six positions in the barchan, there existed variations in barchan migration across these positions. (2) The shape changes at the left horn, right horn, and windward slope of barchans were larger than at the center of the leeward toe and brink, so the estimates based on measurements at these four positions had a weaker fit with the resultant drift potential (RDP) and a greater difference from the resultant drift direction (RDD). (3) The shape of the leeward slope on the barchan did not change much during dune migration, so the center of the leeward toe and brink measurements were closer to the actual dune migration distance and direction. Thus, we recommend using the center of the leeward toe or brink as the optimal measurement points to monitor barchan dune migration. This study will provide a reference for the more accurate measurement of barchan dune migration.

Vehicle State and Road Adhesion Coefficient Joint Estimation Based on High-Order Cubature Kalman Algorithm

With regard to the rear-drive in-wheel motor vehicle, this paper studies the joint estimation method for the vehicle state and road adhesion coefficient. A nonlinear seven degrees of freedom vehicle estimation model and a tire estimation model are established. A vehicle driving state estimator and a road adhesion coefficient estimator based on the generalized high-order cubature Kalman filter (GHCKF) algorithm are designed. The vehicle state estimator combines the vehicle model and the tire model to calculate the vehicle state parameters, provides the state parameters for the road adhesion coefficient estimator, and realizes the real-time estimation of the road adhesion coefficient. The exponential fading memory adaptive algorithm is used to update the measurement noise variance, and we upgrade the GHCKF to the adaptive generalized high-order cubature Kalman filter (AGHCKF), which estimates the vehicle state and road adhesion coefficient. The typical working conditions using the double GHCKF/AGHCKF estimation algorithm were simulated and analyzed. Then, high-and low-speed driving experiments based on typical working conditions were carried out. An integrated navigation system (INS), global positioning system (GPS), and real-time kinematic positioning (RTK) were used to collect the real-time data of the vehicle, and compare them with the estimated values of the joint estimator, to verify the feasibility of the vehicle-state–road-adhesion-coefficient joint estimator. We compared a high-order GHCKF algorithm, high-order improved AGHCKF algorithm, and a cubature Kalman filter (CKF) algorithm, and the simulation and experimental results show that the joint estimator using the CKF, GHCKF, and AGHCKF algorithms can realize the real-time estimation of the vehicle state and the road adhesion coefficient. The AGHCKF algorithm shows the best effectiveness and robustness of the three algorithms.

Automated Quantifiable Assessments of Sensorimotor Function Using an Instrumented Fragile Object.

Accurate assessment of hand dexterity plays a critical role in informing rehabilitation and care of upper-limb hemiparetic stroke patients. Common upper-limb assessments, such as the Box and Blocks Test and Nine Hole Peg Test, primarily evaluate gross motor function in terms of speed. These assessments neglect an individual's ability to finely regulate grip force, which is critical in activities of daily living, such as manipulating fragile objects. Here we present the Electronic Grip Gauge (EGG), an instrumented fragile object that assesses both gross and fine motor function. Embedded with a load cell, accelerometer, and Hall-effect sensor, the EGG measures grip force, acceleration, and relative position (via magnetic fields) in real time. The EGG can emit an audible "break" sound when the applied grip force exceeds a threshold. The number of breaks, transfer duration, and applied forces are automatically logged in real-time. Using the EGG, we evaluated sensorimotor function in implicit grasping and gentle grasping for the non-paretic and paretic hands of 3 hemiparetic stroke patients. For all participants, the paretic hand took longer to transfer the EGG during implicit grasping. For 2 of 3 participants, grip forces were significantly greater for the paretic hand during gentle grasping. Differences in implicit grasping forces were unique to each participant. This work constitutes an important step towards more widespread and quantitative measures of sensorimotor function, which may ultimately lead to improved personalized rehabilitation and better patient outcomes.

Evaluation of the Performance of the Real-Time Precise Point Positioning (RT-PPP) Technique Based on Low-Cost GNSS Observations and Contribution of Multi-GNSS Observations

Bu çalışma, düşük maliyetli GNSS (Küresel Uydu Navigasyon Sistemi) gözlemlerine dayalı RT-PPP (Gerçek-Zamanlı Hassas Nokta Konum Belirleme) tekniğinin konum belirleme performansının araştırılmasını ve yönteme çoklu-GNSS gözlemlerinin katkısını sunmaktadır. Çalışmada, iki adet düşük maliyetli u-blox ZED-F9P alıcısı ve ANN-MB-00 anteni kullanılarak statik ve kinematik modda iki farklı deney gerçekleştirilmiştir. Gerçekleştirilen iki deney setinde de GPS (Küresel Konum Belirleme) ve GPS+Galileo gözlemlerine dayalı olarak IGS03 akışından elde edilen gerçek-zamanlı hassas yörünge ve saat düzeltmelerinden elde edilen ürünlerle RT-PPP çözümleri gerçekleştirilmiş ve bu sonuçlar, GPS+Galileo gözlemlerine dayalı RTK (Gerçek-Zamanlı Kinematik) verileri ile karşılaştırılmıştır. Statik modda gerçekleştirilen deneyin bulguları, çoklu-GNSS gözlemlerinin sadece-GPS gözlemlerine kıyasla yaklaşık olarak 30 dakika daha erken yakınsadığını ve sağa, yukarı ve h bileşenleri için sırasıyla 3, 11 ve 6 cm mertebesinde daha doğru konum bilgisi sağladığını açıkça göstermektedir. Kinematik deney sonuçları ise çoklu-GNSS gözlemlerinin tek sisteme kıyasla elde edilen konum doğruluğunu yatay ve düşey bileşenler için sırasıyla %33 ve %25 oranında iyileştirdiğini ifade etmektedir. Ancak, her iki deneyden elde edilen sonuçların dm mertebesinde olduğu göz önüne alındığında, düşük maliyetli GNSS gözlemlerine dayalı RT-PPP tekniğinin yüksek doğruluk gerektiren jeodezik uygulamalarda yeterli olmayacağını, buna karşılık navigasyon uygulamaları için yeterli doğruluk sağladığı açıktır.

Research on the Body Positioning Method of Bolting Robots Based on Monocular Vision

Aiming at the intelligent design of underground roadway support and the precise positioning of unmanned full excavation faces, a positioning and measurement method of bolt robots based on the monocular vision principle was proposed. In this paper, a vehicle body positioning model based on image data was established. The data were obtained with a camera, and the conversion between image coordinates and world coordinates was carried out through coordinate system conversion. A monocular vision positioning system of the bolt robot was designed, and the simulation’s experimental model was established. Under the simulation’s experimental conditions, the effective positioning distance of the monocular vision positioning system was measured. An experimental platform for the bolt robot was designed, and real-time human positioning data measurement of the vehicle was carried out. The experimental error was analyzed, and the reliability of the method was proven. This method realizes the real-time positioning of underground mines through the bolt robot, improves the accuracy and efficiency of the positioning, and lays a foundation for the positioning control of the heading face and the unmanned bolt robot.

A Pure Electric Driverless Crawler Construction Machinery Walking Method Based on the Fusion SLAM and Improved Pure Pursuit Algorithms.

Driverless technology refers to the technology that vehicles use to drive independently with the help of driverless system under the condition of unmanned intervention. The working environment of construction machinery is bad, and the working conditions are complex. The use of driverless technology can greatly reduce the risk of driver operation, reduce labor costs and improve economic benefits.Aiming at the problem of the GPS positioning signal in the working environment of construction machinery being weak and not able to achieve accurate positioning, this paper uses the fusion SLAM algorithm based on improved NDT to realize the real-time positioning of the whole vehicle through reconstruction of the scene. Considering that the motion characteristics of crawler construction machinery are different from those of ordinary passenger cars, this paper improves the existing pure pursuit algorithm. Simulations and real vehicle tests show that the algorithm combined with the fusion SLAM algorithm can realize the motion control of driverless crawler construction machinery well, complete the tracking of the set trajectory perfectly and have high robustness. Considering that there is no mature walking method of driverless crawler construction machinery for reference, the research of this paper will lay a foundation for the development of driverless crawler construction machinery.

Development and testing of a ground recognition system for tractor field operations

To overcome the problems of poor recognition accuracy, slow response speed, and unsuitable for farmland ground environment based on the traditional ground recognition method of vehicle mathematical model, this paper proposes a ground recognition system for tractor field operation based on wheel acceleration. A wheel acceleration sensor and data acquisition device are designed, and a ground recognition algorithm based on Principal Component Analysis - Genetic Algorithm optimization of an Extreme Learning Machine (PCA-GA-ELM) is investigated. The system can collect acceleration samples and extract statistical features in different frequency bands in real time according to the cycle of wheel rotation and then use the trained recognition model to recognize the ground type. In the actual operation process, this system only needs to collect the acceleration test data of one rotation of the tractor wheels to achieve the recognition of the ground. The test set data show that this system achieves 96 % discrimination accuracy, higher than the 87 % of the ELM model without genetic algorithm optimization and 92 % of the GA-BP neural network model. In addition, the real-time ground recognition test shows that the increase in operation speed will slightly reduce the accuracy of the recognition model, but the overall can still maintain 94–98 % accuracy, indicating that the system has good stability. This system can provide a reference for real-time speed adjustment, implement position, and other control parameters during tractor operation.

Drone Detection and Tracking Using RF Identification Signals.

The market for unmanned aerial systems (UASs) has grown considerably worldwide, but their ability to transmit sensitive information poses a threat to public safety. To counter these threats, authorities, and anti-drone organizations are ensuring that UASs comply with regulations, focusing on strategies to mitigate the risks associated with malicious drones. This study presents a technique for detecting drone models using identification (ID) tags in radio frequency (RF) signals, enabling the extraction of real-time telemetry data through the decoding of Drone ID packets. The system, implemented with a development board, facilitates efficient drone tracking. The results of a measurement campaign performance evaluation include maximum detection distances of 1.3 km for the Mavic Air, 1.5 km for the Mavic 3, and 3.7 km for the Mavic 2 Pro. The system accurately estimates a drone's 2D position, altitude, and speed in real time. Thanks to the decoding of telemetry packets, the system demonstrates promising accuracy, with worst-case distances between estimated and actual drone positions of 35 m for the Mavic 2 Pro, 17 m for the Mavic Air, and 15 m for the Mavic 3. In addition, there is a relative error of 14% for altitude measurements and 7% for speed measurements. The reaction times calculated to secure a vulnerable site within a 200 m radius are 1.83 min (Mavic Air), 1.03 min (Mavic 3), and 2.92 min (Mavic 2 Pro). This system is proving effective in addressing emerging concerns about drone-related threats, helping to improve public safety and security.

Real-time service performances of BDS-3 and Galileo constellations with a linear satellite clock correction models

In order to facilitate high-precision and real-time Precise Point Positioning (PPP), the International GNSS (Global Navigation Satellite System) Service (IGS), BDS-3 (BeiDou-3 Navigation Satellite System), and Galileo navigation satellite system (Galileo) have provided real-time satellite clock correction, which is updated at a high-frequency. However, the frequent updates pose the challenges of increasing the computational burden and compromising the timeliness of these correction parameters. To address this issue, an improved Real-Time Service (RTS) method is developed using an extrapolation algorithm and a linear model. The results indicate that a 1 h arc length of the satellite clock correction series is optimal for fitting a linear model of the RTS. With this approach, the 1 h extrapolation results for BDS-3 and Galileo are superior to 0.09 ns. Moreover, when these model coefficients are transmitted and updated at the intervals of 1, 2, 5, and 10 min, the corresponding PPP can converge at the centimeter-level. It is evident that these improved RTS methods outperform the current approach with high-frequency interval transmission, as they effectively mitigate the challenges associated with maintaining the timeliness of correction parameters.

Real-time UAV Position Control based on Visual Servo Control Algorithm

Real-time UAV Position Control based on Visual Servo Control Algorithm

Registration between 2D and 3D Ultrasound Images to Track Liver Blood Vessel Movement.

For the accurate positioning of surgical tools, conventional intraoperative navigation systems have been developed to recognize the relationship between target positions and the tools. However, since an internal organ is deformed during the operation, registration between realtime two-dimensional (2D) ultrasound images and three-dimensional (3D) CT or MRI images is not always effective. Therefore, this study developed image registration between 2D and 3D ultrasound images considering deformation for tracking target vessel movement in the liver. 3D ultrasound image was obtained in advance with 3D coordinates, including the target vessel. Then real-time 2D images and ultrasound probe position were simultaneously acquired using a 3D position sensor. We applied multiple image resolution registration, where rapid and fine optimizations can be expected at higher and lower levels, respectively. Meanwhile, the gradient descent method was adopted for the optimization, which determines the relative arrangements to obtain maximum similarity between 2D and 3D images. We experimentally established resolution level parameters using a phantom before applying it to track liver blood vessel movements in a normal healthy subject. Comparing the 2D images and the registered images, although the approach has some limitations in tracking large displacement, we confirmed that the cross-section of the target blood vessel was clearly visualized. This method has the potential for an ultrasound therapy targeting blood vessels under natural respiration conditions.

Design and Development of Hydro-Optical Communication-Based Invasive Wireless Sensor Networks

The selection and implementation of a hydro-optical communication connection for real-time position tracking of remotely operated vehicles (ROVs) in underwater settings are the main topics of this research article. For a variety of applications, including pipeline maintenance, environmental research, and undersea infrastructure inspection, effective monitoring and control of ROVs is essential. Due to signal attenuation and interference, conventional communication techniques are difficult to use in aquatic environments. To get over these restrictions, hydro-optical communication, which sends light messages through water, presents a viable option. The paper examines current hydro-optical communication methods and technology, pointing out their benefits and drawbacks. It deals with the technical needs of setting up a trustworthy communication channel for monitoring the real-time location of ROVs. To ensure accurate and timely data delivery, an appropriate communication protocol is created. The research also demonstrates the smooth operation of hydro-optical communication with the ROV's navigation system. The suggested method is put into practise and tested in safe underwater environments in order to assess how well it works at pinpointing the position of ROVs. Among the performance indicators evaluated are data rate, communication range, and energy efficiency. The results of this study help to advance the area of underwater communication and make it easier to monitor and manage ROVs in real-time for a variety of underwater applications.

Development of a spatial orientation model for the actuator of a mechatronic system

Purpose. Development of a control system for a mechatronic system, taking into account the geometric solution of the inverse kinematics problem. The methods. The research was based on the principles of theoretical and applied mechanics, as well as the Denavit-Hartenberg transformation model. The formation of graphical computational models and kinematic diagrams simplified the development of the mathematical model of the dynamic system without considering the forces acting on the mechanism links. The possibility of using matrix transformations to simplify the search for generalized coordinates and transition to the local disposition of each link was explored for further integration of the obtained results into automation and control systems. Findings. The obtained results, determining the trajectory of the gripper using inverse kinematics models, allowed the development of algorithms for determining the position of mechanism links with the possibility of software implementation of the control system. Identifying patterns for determining accurate position coordinates by the matrix method allows the application of open-source software for real-time position calculations. The originality. The use of modern technologies for visual assessment of the external environment and coordinating control impulses of the executive body drive was first achieved based on the mathematical model of the inverse kinematics of a multi-link mechanism. This automated the determination of local coordinates for each link within its degrees of freedom and algorithmized this process. Combining the mathematical models of mechanism kinematics and the matrix form of coordinate search allows investigating the influence of the displacement of the i-th link of the mechatronic technical complex on the overall system, considering the acting force systems and the specified spatial orientation of both the executive body and intermediate links. Practical implementation. The implementation of obtained kinematic models using the matrix method enables the software realization of algorithms for searching the coordinates of the i-th mechanism link, automating the control process with the task of final positions, determining the level of integral error during the displacement of the initial link, and providing the possibility of programming autonomous mechatronic systems with open-source code. As a result, the introduction of semi or fully autonomous technical complexes will automate technological processes in various industries.

딥러닝 기반 이미지분석 기술을 활용한 3차원 역학실험장치 개발

This research proposes to develop a device that fuses deep learning-based object detection technology and measurement algorithms to collect real-time position data of objects moving in an area. The commonly used motion analysis devices are limited to measuring two-dimensional plane motion based on images. Therefore, physical quantities such as moments of inertia and rotation are difficult to obtain. Although systems that track, measure, and analyze the motion of objects are used in sports, they are expensive and specific to the field, making it difficult to use them widely in various fields of study. Thus, this research aims to overcome these limitations by developing device that analyzes the movement of objects in various three-dimensional spaces using two vertically positioned cameras, which can be used in a variety of environments with dramatically reduced costs. The proposed device consists of two main steps: the first step is to measure the three-dimensional position of an object with a camera connected to a single-shot multibox detector (SSD) trained with 1,465 image data, and the second step is to calculate the physical quantity and analyze the motion after visualizing the data of the measured object’s position and time in three dimensions. To measure the actual coordinates of the object, the diameter of the object is converted into pixels, and the distance in centimeters per pixel is obtained by utilizing the similarity ratio with the moving object captured by the camera. With the cm/px calculated, the pixel value obtained through camera is converted to cm, the actual coordinate of the object. To verify the accuracy of the proposed data, the acceleration of gravity was measured with the trend line of each parabola. The average error range of the acceleration of gravity calculated was ±0.20 m/s², which demonstrated great accuracy.