Position Estimation Method Research Articles

Position and orientation estimation method based on 3D digital morphology contour registration

Abstract Accurately and quickly obtaining the positions and orientations of mechanical parts based on the digital morphologies of mechanical parts are the key to achieving efficient and accurate assembly of mechanical parts. However, due to poor robustness and compactness in extracting digital morphology contours of mechanical parts, the accuracy of assembly positions and orientations obtained by using digital morphology contours cannot meet the requirements of high-precision assembly. Therefore, this paper proposes a position and orientation estimation method based on 3D digital morphology contour registration. This method extracts and optimizes digital morphology contours of mechanical parts and obtains the assembly positions and orientations by using an improved iterative closest point method to register the extracted digital morphology contours with those of the mechanical parts in assembly targets with desired positions and orientations. Experiments are conducted using mechanical parts from the ABC dataset and inertial confinement fusion micro-target. From the experimental results, when using the assembly positions and orientations obtained through the method proposed in this paper to assemble mechanical parts, it can achieve translation absolute errors of 8 μm, 5 μm, and 9 μm along the X-, Y-, and Z-axes, respectively. Similarly, the angular absolute errors in rotations around the Z-, Y-, and X-axes can be less than or equal to 0.16°, 0.15°, and 0.11°, respectively. The results prove that the proposed method in this paper exhibits high computational efficiency and accuracy, providing an effective approach for digital assembly of mechanical parts.

An Improved Rotor Position and Speed Estimation Method for PMSM with Hall Sensors

An Improved Rotor Position and Speed Estimation Method for PMSM with Hall Sensors

Precise position estimation methods for differential-IRNSS using iterative algorithm

Precise position estimation methods for differential-IRNSS using iterative algorithm

Attitude estimation for an all-rotating monocopter through attitude decomposition and MARG Sensor fusion

A monocopter is a biology-inspired aircraft based on samara. Due to the constant high spin during flight, attitude estimation is highly challenging. The difficulty of attitude estimation is extracting the effective gravity acceleration information from the large centripetal acceleration interference. Based on the characteristics of monocopters, our study proposes a method to accomplish monocopter attitude estimation through onboard sensor fusion. Our method decomposes the body attitude angles into two-disc attitude angles and three relative attitude angles. Through five steps (a priori body attitude estimation, relative attitude calculation, gravity acceleration information extraction, body attitude estimation through data fusion, and disc attitude estimation), a low-cost attitude determination method relying only on magnetic, angular rate, and gravity sensors is achieved. Finally, the performance of our proposed attitude estimation method is demonstrated through tests on a rotational platform and three degrees of freedom virtual flight testing of a monocopter. The test results show that the attitude estimation method has high precision and the ability for practical applications.

Artificial intelligence facilitates the potential of simulator training: An innovative laparoscopic surgical skill validation system using artificial intelligence technology.

The development of innovative solutions, such as simulator training and artificial intelligence (AI)-powered tutoring systems, has significantly changed surgical trainees' environments to receive the intraoperative instruction necessary for skill acquisition. In this study, we developed a new objective assessment system using AI for forceps manipulation in a surgical training simulator. Laparoscopic exercises were recorded using an iPad®, which provided top and side views. Top-view movies were used for AI learning of forceps trajectory. Side-view movies were used as supplementary information to assess the situation. We used an AI-based posture estimation method, DeepLabCut (DLC), to recognize and positionally measure the forceps in the operating field. Tracking accuracy was quantitatively evaluated by calculating the pixel differences between the annotation points and the points predicted by the AI model. Tracking stability at specified key points was verified to assess the AI model. We selected a random sample to evaluate tracking accuracy quantitatively. This sample comprised 5% of the frames not used for AI training from the complete set of video frames. We compared the AI detection positions and correct positions and found an average pixel discrepancy of 9.2. The qualitative evaluation of the tracking stability was good at the forceps hinge; however, forceps tip tracking was unstable during rotation. The AI-based forceps tracking system can visualize and evaluate laparoscopic surgical skills. Improvements in the proposed system and AI self-learning are expected to enable it to distinguish the techniques of expert and novice surgeons accurately. This system is a useful tool for surgeon training and assessment.

High-Performance Position Estimation Method Based on the Error-Corrected State Observer and Its Application on Permanent Magnet Synchronous Machine

High-Performance Position Estimation Method Based on the Error-Corrected State Observer and Its Application on Permanent Magnet Synchronous Machine

A Sensor-Free Control System for a High-Frequency Synchronous Motor with Incorporated Magnets Based on a Combined Method of Position Estimation

A Sensor-Free Control System for a High-Frequency Synchronous Motor with Incorporated Magnets Based on a Combined Method of Position Estimation

A New Attitude Estimation Method for Spherical Reluctance Motor Based on Few Flux Linkage Samples

A New Attitude Estimation Method for Spherical Reluctance Motor Based on Few Flux Linkage Samples

Convolutional neural network for improved event-based Shack-Hartmann wavefront reconstruction

Shack-Hartmann wavefront sensing is a technique for measuring wavefront aberrations, whose use in adaptive optics relies on fast position tracking of an array of spots. These sensors conventionally use frame-based cameras operating at a fixed sampling rate to report pixel intensities, even though only a fraction of the pixels have signal. Prior in-lab experiments have shown feasibility of event-based cameras for Shack-Hartmann wavefront sensing (SHWFS), asynchronously reporting the spot locations as log intensity changes at a microsecond time scale. In our work, we propose a convolutional neural network (CNN) called event-based wavefront network (EBWFNet) that achieves highly accurate estimation of the spot centroid position in real time. We developed a custom Shack-Hartmann wavefront sensing hardware with a common aperture for the synchronized frame- and event-based cameras so that spot centroid locations computed from the frame-based camera may be used to train/test the event-CNN-based centroid position estimation method in an unsupervised manner. Field testing with this hardware allows us to conclude that the proposed EBWFNet achieves sub-pixel accuracy in real-world scenarios with substantial improvement over the state-of-the-art event-based SHWFS. An ablation study reveals the impact of data processing, CNN components, and training cost function; and an unoptimized MATLAB implementation is shown to run faster than 800 Hz on a single GPU.

Object pose estimation method for robotic arm grasping

To address the issue of target detection in the planar grasping task, a position and attitude estimation method based on YOLO-Pose is proposed. The aim is to detect the three-dimensional position of the spacecraft’s center point and the planar two-dimensional attitude in real time. First, the weight is trained through transfer learning, and the number of key points is optimized by analyzing the shape characteristics of the spacecraft to improve the representation of pose information. Second, the CBAM dual-channel attention mechanism is integrated into the C3 module of the backbone network to improve the accuracy of pose estimation. Furthermore, the Wing Loss function is used to mitigate the problem of random offset in key points. The incorporation of the bi-directional feature pyramid network (BiFPN) structure into the neck network further improves the accuracy of target detection. The experimental results show that the average accuracy value of the optimized algorithm has increased. The average detection speed can meet the speed and accuracy requirements of the actual capture task and has practical application value.

Estimation of log-gripping position using instance segmentation for autonomous log loading

ABSTRACT Autonomous forestry machinery is necessary both to ensure safety and improve productivity. Previous research related to automation technology for forestry machinery has mainly focused on autonomous driving; research on log loading/unloading is still in progress. To automate the loading and unloading of logs, it is necessary to evaluate the errors of several processes quantitatively: detecting logs in the environment, estimating the gripping position, and controlling the machine. This paper focuses on the development of an autonomous log loading operation. This study aims to propose an estimation method for log gripping position based on log detection using instance segmentation. Evaluation of the proposed system shows that the root mean square errors in the radial, axial, and vertical directions are 0.162, 1.526, and 0.140 m for sparse logs, 0.384, 0.271, and 0.119 m for dense logs, and 0.764, 1.022, and 0.194 m for unorganized logs, respectively. Our results demonstrate that the proposed method is sufficiently accurate to achieve gripping of a single log; however, the accuracy is insufficient for gripping one in a dense group of logs accurately.

Measurement of Sedentary Behavior-The Outcomes of the Angle for Posture Estimation (APE) Method.

Hip-worn accelerometers are commonly used to assess habitual physical activity, but their accuracy in precisely measuring sedentary behavior (SB) is generally considered low. The angle for postural estimation (APE) method has shown promising accuracy in SB measurement. This method relies on the constant nature of Earth's gravity and the assumption that walking posture is typically upright. This study investigated how cardiorespiratory fitness (CRF) and body mass index (BMI) are related to APE output. A total of 3475 participants with adequate accelerometer wear time were categorized into three groups according to CRF or BMI. Participants in low CRF and high BMI groups spent more time in reclining and lying postures (APE ≥ 30°) and less time in sitting and standing postures (APE < 30°) than the other groups. Furthermore, the strongest partial Spearman correlation with CRF (r = 0.284) and BMI (r = -0.320) was observed for APE values typical for standing. The findings underscore the utility of the APE method in studying associations between SB and health outcomes. Importantly, this study emphasizes the necessity of reserving the term "sedentary behavior" for studies wherein the classification of SB is based on both intensity and posture.

Position Estimation Method for Unmanned Tracked Vehicles Based on a Steering Dynamics Model

A position estimation method for unmanned tracked vehicles based on a steering dynamics model was developed during this study. This method can be used to estimate the position of a tracked vehicle in real time without relying on a high-precision positioning system. First, the relationship between the shear displacement of the track relative to the ground and the speed and yaw rate of the tracked vehicle during the steering process was analyzed. Next, the steering force of the tracked vehicle was calculated by using the shear force–displacement theory, and a steering dynamics model considering the acceleration of the vehicle was established. The experimental results show that this steering dynamics model produced more accurate position estimations for an unmanned tracked vehicle than did the kinematics model. This method can serve as a reference for the positioning of unmanned tracked vehicles working in special environments that cannot use precise positioning systems.

An Improved Rotor Position Estimation Method for SPMSM With Misaligned Hall-Effect Sensor

This paper presents an improved rotor position estimation method for surface permanent magnet synchronous motor (SPMSM) using a low-resolution Hall-Effect sensor. Firstly, an observation function is proposed to detect the speed variation, and a novel correction method based on optimization process is proposed to identify and correct the deviation angle caused by sensor misalignment. Then, an improved position observer is introduced using a gain-scheduling controller to achieve better performance in both steady state and transient state. The proposed method enhances the anti-perturbation and start-up capability for SPMSM and compensate the misalignment effect of Hall-Effect sensors. Experimental results are demonstrated to verify the effectiveness of the proposed method.

Бездатчиковая система управления высокочастотным синхронным двигателем с инкорпорированными магнитами на основе комбинированного способа оценки положения

The known methods to estimate the position in drives based on synchronous motors with incorporated magnets have significant limitations in accuracy and noise immunity at high frequencies of the main voltage harmonic. Due to the requirements to improve the weight, size, and cost indicators of electric drives, it is relevant to design a sensorless synchronous drive operating with frequencies from 0 to 1000 Hz and more in the presence of a wide 5…10:1 speed control zone with a constant nominal power. The results of the study are based on the analysis of theoretical and experimental data obtained by other authors, as well as computer modeling in the Delphi software environment. The initial information for the simulation is obtained from the technical description guide and data of a full-scale experiment on a real-life object. A vector control system has been developed for a high-frequency synchronous motor with incorporated magnets without a mechanical coordinate sensor based on a combination of speed and position estimation methods by means of high-frequency injection and EMF calculation. The study of the developed system is carried out by means of modeling, considering the influence of interference in the measuring channels and the effect of cross-saturation of the magnetic circuit. The proposed structure and control algorithms have provided operability and high-quality control indicators sufficient to design a traction drive with a maximum frequency of 800 Hz or more and an area of operation in a constant rated power mode of at least 8:1 with a signal-to-noise ratio characteristic of typical current sensors.

A Rotor Position Detection Method for Permanent Magnet Synchronous Motors Based on Variable Gain Discrete Sliding Mode Observer

The purpose of this paper is to study the sensor-less rotor position estimation method for permanent magnet synchronous motors, and to achieve accurate estimation of rotor position in different conditions. Firstly, the traditional super-twisting observer algorithm is analyzed, and a new discrete variable gain sliding mode observer is designed to solve the buffeting problem in discrete systems, taking the reaction force as the disturbance signal. By estimating the back potential of the observer, the buffeting problem in the sliding mode algorithm can be effectively improved as shown by the simulation results. Then, to solve the problem of phase delay in rotor position estimation, an adaptive orthogonal phase-locked loop method is used to compensate the estimation error caused by the change in motor speed and increase the estimation accuracy of rotor position. The stability of the method can be proven by Lyapunov’s second method. Simulation experiments verify the accuracy of the proposed PMSM rotor position estimation method.

Airborne distributed array position calibration using sparse spatial-frequency accompanying flight MISO array

Airborne coherent distributed array radar (DCAR) can greatly improve radar detection range and angle resolution. However, the time-variant property of airborne DCAR elements’ position strongly limits the application in real situations. In this paper, we developed a high-accuracy position estimation method for airborne DCAR systems with position errors using a single accompanying flight (AF) array. The basic principle is using a small array with a spatial sparse configuration to transmit a sparse-frequency multiple input single output (MISO) signal. Then airborne DCAR elements receive the signals and perform multi-channel demodulation and parameter estimation processing to obtain their distance and angle information relative to the AF array. The presented method estimates the position of all DACR elements with the single same array and thus various inaccordance between different transmit sources can be avoided. By using a spatial-frequency sparse array, the system complexity of the accompanying flight platform can be greatly reduced. Our method can obtain position estimates that are smaller than the operating wavelength, which ensures that sufficient performance can be obtained from coherent processing. Simulation results are given to demonstrate the effectiveness of the proposed method.

Geolocalization from Aerial Sensing Images Using Road Network Alignment

Estimating the geographic positions in GPS-denied environments is of great significance to the safe flight of unmanned aerial vehicles (UAVs). In this paper, we propose a novel geographic position estimation method for UAVs after road network alignment. We discuss the generally overlooked issue, namely, how to estimate the geographic position of the UAV after successful road network alignment, and propose a precise robust solution. In our method, the optimal initial solution of the geographic position of the UAV is first estimated from the road network alignment result, which is typically presented as a homography transformation between the observed road map and the reference one. The geographic position estimation is then modeled as an optimization problem to align the observed road with the reference one to improve the estimation accuracy further. Experiments on synthetic and real flight aerial image datasets show that the proposed algorithm can estimate more accurate geographic position of the UAV in real time and is robust to the errors from homography transformation estimation compared to the currently commonly-used method.

Online Inductance Identification of Permanent Magnet Synchronous Motors Independent of Rotor Position Information

Sensorless control of permanent magnet synchronous motors is preferable in some applications due to cost and mounting space concerns. The performance of most existing position estimation methods greatly depends on the accuracy of the motor inductance. As the estimated position should not be involved in the parameter identification process in a sensorless control system, an online inductance identification method independent of the rotor position information is developed in this paper. The proposed method utilizes the recursive least square algorithm and the particle swarm optimization algorithm to realize real-time identification of the inductance along the direct axis and the quadrature axis, respectively, based on the deduced parametric equations without position information. The proposed method is efficient enough to be implemented within 0.2 ms and does not introduce any additional signal injection. A test bench is built to validate the characteristics of the method, and the experimental results show that the identified inductance can converge to the actual value rapidly and is robust to changes in the initial values and stator current. With the proposed method, accurate estimation of the rotor position and speed can be obtained using traditional model-based position estimators, and the stability of the sensorless control system can be improved significantly.

A Study on the UWB-Based Position Estimation Method Using Dead Reckoning Information for Active Driving in a Mapless Environment of Intelligent Wheelchairs

As the world enters an aging and super-aged society, the application of advanced technology in assistive devices to support the daily life of the elderly is becoming a hot issue. Among them, electric wheelchairs are representative assistive devices for the walking support of the elderly, and their structural form is similar to AGV and AMR. For this reason, research is being introduced and underway to guarantee the right to voluntarily move or improve the convenience of movement for the elderly and severely disabled people who have difficulties in operating a joystick for operating an electric wheelchair. Autonomous driving of mobile robots is a technology that configures prior information on the driving environment as a map DB and operates based on it. However, active driving assistance technology is needed because wheelchairs do not move in a limited space, but can move to a place without a prior map DB or vehicle boarding depending on the passenger’s intention to move. Therefore, a system for estimating the moving position and direction of the wheelchair is needed to develop a driving assistance technology in the relevant driving environment. In order to solve the above problem, this study proposes a position and direction estimation algorithm suitable for active driving of a wheelchair based on a UWB sensor. This proposal is an algorithm for estimating the position and direction of the wheelchair through the fusion of UWB, IMU, and encoder sensors. In this proposal, it is difficult to design an active driving assistance system for wheelchairs due to low accuracy, obstacles, and errors due to signal strength in the position and direction estimation with UWB sensors alone. Therefore, this study proposes a wheelchair driving position and direction estimation system that fuses the dead recording information of a wheelchair and the UWB-based position estimation technique based on sensors applied in IMU and encoders. Applying quantitative verification to the proposed technique, the direction estimation accuracy of the wheelchair of about 15.3° and the position estimation error average of ±15 cm were confirmed, and it was verified that a driving guide for active driving was possible when the sensor system proposed in a mapless environment of the wheelchair was installed at a specific destination.