Improving the Accuracy of Estimates of Indoor Distance Moved Using Deep Learning-Based Movement Status Recognition

As a result of the development of wireless indoor positioning techniques such as WiFi, Bluetooth, and Ultra-wideband (UWB), the positioning traces of moving people or objects in indoor environments can be tracked and recorded, and the distances moved can be estimated from these data traces. These estimates are very useful in many applications such as workload statistics and optimized job allocation in the field of logistics. However, due to the uncertainties of the wireless signal and corresponding positioning errors, accurately estimating movement distance still faces challenges. To address this issue, this paper proposes a movement status recognition-based distance estimating method to improve the accuracy. We divide the positioning traces into segments and use an encoder–decoder deep learning-based model to determine the motion status of each segment. Then, the distances of these segments are calculated by different distance estimating methods based on their movement statuses. The experiments on the real positioning traces demonstrate the proposed method can precisely identify the movement status and significantly improve the distance estimating accuracy.

Effects of harvester positioning errors on merchantable timber volume predicted and estimated from airborne laser scanner data in mature Norway spruce forests

Newly developed positioning systems in cut-to-length harvesters enable georeferencing of individual trees with submeter accuracy. Together with detailed tree measurements recorded during processing of the tree, georeferenced harvester data are emerging as a valuable tool for forest inventory. Previous studies have shown that harvester data can be linked to airborne laser scanner (ALS) data to estimate a range of forest attributes. However, there is little empirical evidence of the benefits of improved positioning accuracy of harvester data. The two objectives of this study were to (1) assess the accuracy of timber volume estimation using harvester data and ALS data acquired with different scanners over multiple years and (2) assess how harvester positioning errors affect merchantable timber volume predicted and estimated from ALS data. We used harvester data from 33 commercial logging operations, comprising 93 731 harvested stems georeferenced with sub-meter accuracy, as plot-level training data in an enhanced area-based inventory approach. By randomly altering the tree positions in Monte Carlo simulations, we assessed how prediction and estimation errors were influenced by different combinations of simulated positioning errors and grid cell sizes. We simulated positioning errors of 1, 2, …, 15 m and used grid cells of 100, 200, 300 and 400 m. Values of root mean square errors obtained for cell-level predictions of timber volume differed significantly for the different grid cell sizes. The use of larger grid cells resulted in a greater accuracy of timber volume predictions, which were also less affected by positioning errors. Accuracies of timber volume estimates at logging operation level decreased significantly with increasing levels of positioning error. The results highlight the benefit of accurate positioning of harvester data in forest inventory applications. Further, the results indicate that when estimating timber volume from ALS data and inaccurately positioned harvester data, larger grid cells are beneficial.2

Enhanced location tracking in sensor fusion-assisted virtual reality micro-manipulation environments

Virtual reality (VR) technology plays a significant role in many biomedical applications. These VR scenarios increase the valuable experience of tasks requiring great accuracy with human subjects. Unfortunately, commercial VR controllers have large positioning errors in a micro-manipulation task. Here, we propose a VR-based framework along with a sensor fusion algorithm to improve the microposition tracking performance of a microsurgical tool. To the best of our knowledge, this is the first application of Kalman filter in a millimeter scale VR environment, by using the position data between the VR controller and an inertial measuring device. This study builds and tests two cases: (1) without sensor fusion tracking and (2) location tracking with active sensor fusion. The static and dynamic experiments demonstrate that the Kalman filter can provide greater precision during micro-manipulation in small scale VR scenarios.

Optimal Controller Design for Ultra-Precision Fast-Actuation Cutting Systems

Fast-actuation cutting systems are in high demand for machining of freeform optical parts. Design of such motion systems requires good balance between structural hardware and controller design. However, the controller tuning process is mostly based on human experience, and it is not feasible to predict positioning performance during the design stage. In this paper, a deterministic controller design approach is adopted to preclude the uncertainty associated with controller tuning, which results in a control law minimizing positioning errors based on plant and disturbance models. Then, the influences of mechanical parameters such as mass, damping, and stiffness are revealed within the closed-loop framework. The positioning error was reduced from 1.19 nm RMS to 0.68 nm RMS with the new controller. Under the measured disturbance conditions, the optimal bearing stiffness and damping coefficient are 1.1×105 N/m and 237.7 N/(m·s−1), respectively. We also found that greater moving inertia helps to reduce all disturbances at high frequencies, in agreement with the positioning experiments. A quantitative understanding of how plant structural parameters affect positioning stability is thus shown in this paper. This is helpful for the understanding of how to reduce error sources from the design point of view.

Effects of the 12 May 2021 Geomagnetic Storm on Georeferencing Precision

In this work, we present the positioning error analysis of the 12 May 2021 moderate geomagnetic storm. The storm happened during spring in the northern hemisphere (fall in the south). We selected 868 GNSS stations around the globe to study the ionospheric and the apparent position variations. We compared the day of the storm with the three previous days. The analysis shows the global impact of the storm. In the quiet days, 93% of the stations had 3D errors less than 10 cm, while during the storm, only 41% kept this level of accuracy. The higher impact was over the Up component. Although the stations have algorithms to correct ionospheric disturbances, the inaccuracies lasted for nine hours. The most severe effects on the positioning errors were noticed in the South American sector. More than 60% of the perturbed stations were located in this region. We also studied the effects produced by two other similar geomagnetic storms that occurred on 27 March 2017 and on 5 August 2019. The comparison of the storms shows that the effects on position inaccuracies are not directly deductible neither from the characteristics of geomagnetic storms nor from enhancement and/or variations of the ionospheric plasma.

Positioning Errors Measurement of CNC Machine Tools Based on J-DBB Method

Due to the errors of the servo system and the errors of the ball screw drive system, the positioning errors inevitably occur in the process of CNC machine tools. The measurement of traditional equipment is limited by a fixed measurement radius and a single degree of freedom, which can only be measured within a fixed plane. In this paper, four different positioning errors of CNC machine tools are first measured at full scale by using J-DBB (a modified double ball bar with one spherical joint connecting two bars) method. The J-DBB device uses a three-degree-of-freedom spherical joint as a connecting part, which realizes that the measurement radius can be continuously changed, and the measurement space is a spatial sphere. First, the principle of the J-DBB method is briefly introduced. Next, four typical positioning errors of CNC machine tools are analyzed and examined, which contain the uniform contraction error of ball screw and linear grating, periodic error of the ball screw and linear grating, interference of measurement devices error, and opposite clearance error. In the end, the trajectories of the CNC machine tool spindle with a single positioning error are simulated by using the J-DBB method. The results reveal that this method can be used for the positioning error of machine tools, which helps to better understand the spatial distribution of CNC machine tool errors and provides guidance for the reasonable selection of working areas to improve the machining accuracy of parts.

Comparison of MLC Positioning Deviations Using Log Files and Establishment of Specific Assessment Parameters for Different Accelerators With IMRT and VMAT

Background and purpose: The study evaluated the differences in leaf positioning deviations by the log files of three advanced accelerators with two delivery techniques, and established specific assessment parameters of leaf positioning deviations for different types of accelerators.Methods: A total of 300 treatment plans with 5 consecutive treatment log files were collected from the Trilogy, TrueBeam and Halcyon accelerators. 50 IMRT and 50 VMAT plans were selected randomly on each accelerator. The log files information was parsed by SunCheck software from Sun Nuclear Corporation. The maximum leaf RMS errors, 95th percentile errors and percentages of different leaf positioning errors were statistically analyzed. The correlations between these evaluation parameters and accelerator performance parameters (maximum leaf speed, mean leaf speed, gantry and arc angle) were analyzed.Results: The average maximum leaf RMS errors of the Trilogy in the IMRT and VMAT plans were 0.45±0.1mm and 0.80±0.07mm, respectively, which were higher than the TrueBeam's 0.03±0.01mm, 0.03±0.01 mm and the Halcyon's 0.06±0.01 mm, 0.07±0.01mm. Similar data results were shown in the 95th percentile error. The maximum leaf RMS errors were strongly correlated with the 95th percentile errors. The leaf positioning deviations in VMAT were higher than those in IMRT for all accelerators. In TrueBeam and Halcyon, leaf position errors above 1 mm were not found in IMRT and VMAT plans. The main influencing factor of leaf positioning deviation was the leaf speed, which has no correlation with gantry and arc angles.Conclusions: Compared with the quality assurance guidelines, the MLC positioning deviations tolerances of the three accelerators should be tightened. For both IMRT and VMAT techniques, the 95th percentile error and the maximum RMS error are suggested to be tightened to 1.5 mm and 1 mm for the Trilogy accelerator respectively. In TrueBeam and Halcyon accelerators, the 95th percentile error and maximum RMS error of 1 mm and 0.5 mm, respectively, are considered appropriate.

Simulation of Crane Trolley Motion Control to Reduce Load Sway

the work aims to develop a method for controlling a crane trolley, providing a decrease in the swinging of the load on the rope at a given positioning accuracy and minimum time for moving the load. Computer simulation of the trolley controlled movement with a load on a rope was carried out within the "Universal Mechanism" software. The 2D computer model includes a trolley with a pulley, a rope with a hook, and a load. T-force represents the wind force effect. The control system (CS) contains three blocks that ensure the movement of the trolley with limited speed and acceleration, the positioning accuracy of the trolley, limiting the deviation and acceleration of a load. List of requirements for the crane CS comprises six issues. The simulation results illustrate the satisfactory performance of the proposed method. The swing angle and trolley positioning accuracy can be controlled within the acceptable range and the external wind disturbance on the load can be successfully suppressed. The optimal parameters of the proportional-differential corrective control, which ensure the minimum travel time of the trolley with limited oscillation of the load, have been determined. The optimal values of the control parameters depend on the inertial characteristics of the load. The maximum permissible values of speed, acceleration, and positioning errors of the trolley limit from above the optimal values of the control parameters.