Development of a Three-Fingered Jamming Gripper for Corresponding to the Position Error and Shape Difference

2019 ◽  
Author(s):  
Kohei Amano ◽  
Yukiko Iwasaki ◽  
Koki Nakabayashi ◽  
Hiroyasu Iwata
Keyword(s):  
Position Error ◽  
Shape Difference

scholarly journals A Calibration Method of Phase Error Caused by Target’s Position Error

2021 ◽  
Vol 187 ◽  
pp. 188-193
Author(s):  
Fang Liu ◽  
Ming Lyn ◽  
Haohao Hou
Keyword(s):  
Phase Error ◽  
Calibration Method ◽  
Position Error

scholarly journals Consistency of the Empirical Distributions of Navigation Positioning System Errors with Theoretical Distributions—Comparative Analysis of the DGPS and EGNOS Systems in the Years 2006 and 2014

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 31
Author(s):  
Mariusz Specht
Keyword(s):  
Normal Distribution ◽  
Root Mean Square ◽  
Statistical Tests ◽  
Position Error ◽  
Positioning System ◽  
Navigation Systems ◽  
Mean Square ◽  
Positioning Systems ◽  
Position Errors ◽  
System Errors

Positioning systems are used to determine position coordinates in navigation (air, land and marine). The accuracy of an object’s position is described by the position error and a statistical analysis can determine its measures, which usually include: Root Mean Square (RMS), twice the Distance Root Mean Square (2DRMS), Circular Error Probable (CEP) and Spherical Probable Error (SEP). It is commonly assumed in navigation that position errors are random and that their distribution are consistent with the normal distribution. This assumption is based on the popularity of the Gauss distribution in science, the simplicity of calculating RMS values for 68% and 95% probabilities, as well as the intuitive perception of randomness in the statistics which this distribution reflects. It should be noted, however, that the necessary conditions for a random variable to be normally distributed include the independence of measurements and identical conditions of their realisation, which is not the case in the iterative method of determining successive positions, the filtration of coordinates or the dependence of the position error on meteorological conditions. In the preface to this publication, examples are provided which indicate that position errors in some navigation systems may not be consistent with the normal distribution. The subsequent section describes basic statistical tests for assessing the fit between the empirical and theoretical distributions (Anderson-Darling, chi-square and Kolmogorov-Smirnov). Next, statistical tests of the position error distributions of very long Differential Global Positioning System (DGPS) and European Geostationary Navigation Overlay Service (EGNOS) campaigns from different years (2006 and 2014) were performed with the number of measurements per session being 900’000 fixes. In addition, the paper discusses selected statistical distributions that fit the empirical measurement results better than the normal distribution. Research has shown that normal distribution is not the optimal statistical distribution to describe position errors of navigation systems. The distributions that describe navigation positioning system errors more accurately include: beta, gamma, logistic and lognormal distributions.

scholarly journals Research of Calibration Method for Industrial Robot Based on Error Model of Position

2021 ◽  
Vol 11 (3) ◽  
pp. 1287
Author(s):  
Tianyan Chen ◽  
Jinsong Lin ◽  
Deyu Wu ◽  
Haibin Wu
Keyword(s):  
Coordinate System ◽  
Industrial Robot ◽  
Calibration Method ◽  
Error Model ◽  
Position Error ◽  
Industrial Robots ◽  
Positioning Error ◽  
Robot Position ◽  
General Measurement ◽  
Parameter Error

Based on the current situation of high precision and comparatively low APA (absolute positioning accuracy) in industrial robots, a calibration method to enhance the APA of industrial robots is proposed. In view of the "hidden" characteristics of the RBCS (robot base coordinate system) and the FCS (flange coordinate system) in the measurement process, a comparatively general measurement and calibration method of the RBCS and the FCS is proposed, and the source of the robot terminal position error is classified into three aspects: positioning error of industrial RBCS, kinematics parameter error of manipulator, and positioning error of industrial robot end FCS. The robot position error model is established, and the relation equation of the robot end position error and the industrial robot model parameter error is deduced. By solving the equation, the parameter error identification and the supplementary results are obtained, and the method of compensating the error by using the robot joint angle is realized. The Leica laser tracker is used to verify the calibration method on ABB IRB120 industrial robot. The experimental results show that the calibration method can effectively enhance the APA of the robot.

Immediate effects of thoracic manipulation on cervical joint position sense in individuals with mechanical neck pain: A randomized controlled trial

2021 ◽  
pp. 1-9
Author(s):  
Emin Ulas Erdem ◽  
Banu Ünver ◽  
Eda Akbas ◽  
Gizem Irem Kinikli
Keyword(s):  
Neck Pain ◽  
Range Of Motion ◽  
Position Sense ◽  
Joint Position Sense ◽  
Position Error ◽  
Cervical Region ◽  
Joint Position ◽  
Cervical Range Of Motion ◽  
Arm Pain ◽  
Neck Function

BACKGROUND: Performing thoracic manipulations for neck pain can result in immediate improvements in neck function. OBJECTIVE: The aim of this study was to investigate the immediate effects of thoracic manipulation on cervical joint position sense and cervical range of motion in individuals with chronic mechanical neck pain. METHODS: Eighty male volunteers between 18–25 years and having chronic or recurrent neck or shoulder pain of at least 3 months duration with or without arm pain were randomized into two groups: Thoracic Manipulation Group (TMG:50) and Control Group (CG:30), with a pretest-posttest experimental design. The TMG was treated with thoracic extension manipulation while the CG received no intervention. Cervical joint position error and cervical range of motion of the individuals were assessed at baseline and 5 minutes later. RESULTS: There was no difference in demographic variables such as age (p= 0.764), Body Mass Index (p= 0.917) and Neck Pain Disability Scale (NPDS) scores (p= 0.436) at baseline outcomes between TMG and CGs. Joint position error outcomes between the two groups following intervention were similar in all directions at 30 and 50 degrees. Differences in range of motion following intervention in neck flexion (p< 0.001) and right rotation (p= 0.004) were higher in TMG compared to CG. CONCLUSIONS: A single session of thoracic manipulation seems to be inefficient on joint position sense in individuals with mild mechanical neck pain. However, thoracic manipulation might be an effective option to increase flexion and rotation of the cervical region as an adjunctive to treatment.

scholarly journals Data-Driven Object Vehicle Estimation by Radar Accuracy Modeling with Weighted Interpolation

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2317
Author(s):  
Woo Young Choi ◽  
Jin Ho Yang ◽  
Chung Choo Chung
Keyword(s):  
Measurement Uncertainty ◽  
Relative Velocity ◽  
Tracking Error ◽  
Estimation Method ◽  
Position Error ◽  
Data Driven ◽  
Weighted Interpolation ◽  
Design Data ◽  
Feasible Set ◽  
Estimation Scheme

For accurate object vehicle estimation using radar, there are two fundamental problems: measurement uncertainties in calculating an object’s position with a virtual polygon box and latency due to commercial radar tracking algorithms. We present a data-driven object vehicle estimation scheme to solve measurement uncertainty and latency problems in radar systems. A radar accuracy model and latency coordination are proposed to reduce the tracking error. We first design data-driven radar accuracy models to improve the accuracy of estimation determined by the object vehicle’s position. The proposed model solves the measurement uncertainty problem within a feasible set for error covariance. The latency coordination is developed by analyzing the position error according to the relative velocity. The position error by latency is stored in a feasible set for relative velocity, and the solution is calculated from the given relative velocity. Removing the measurement uncertainty and latency of the radar system allows for a weighted interpolation to be applied to estimate the position of the object vehicle. Our method is tested by a scenario-based estimation experiment to validate the usefulness of the proposed data-driven object vehicle estimation scheme. We confirm that the proposed estimation method produces improved performance over the conventional radar estimation and previous methods.

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