A new misalignment calibration method of portable geomagnetic field vector measurement system

Abstract Robot calibration plays an increasingly important role in manufacturing. For robot calibration on the manufacturing floor, it is desirable that the calibration technique be easy and convenient to implement. This paper presents a new self-calibration method to calibrate and compensate for robot system kinematic errors. Compared with the traditional calibration methods, this calibration method has several unique features. First, it is not necessary to apply an external measurement system to measure the robot end-effector position for the purpose of kinematic identification since the robot measurement system has a sensor as its integral part. Second, this self-calibration is based on distance measurement rather than absolute position measurement for kinematic identification; therefore the calibration of the transformation from the world coordinate system to the robot base coordinate system, known as base calibration, is not necessary. These features not only greatly facilitate the robot system calibration but also shorten the error propagation chain, therefore, increase the accuracy of parameter estimation. An integrated calibration system is designed to validate the effectiveness of this calibration method. Experimental results show that after calibration there is a significant improvement of robot accuracy over a typical robot workspace.

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High-Precision Magnetic Field Vector Measurement Based on the Quantum Coherent Superposition States

2018 Conference on Precision Electromagnetic Measurements (CPEM 2018) ◽

10.1109/cpem.2018.8501244 ◽

2018 ◽

Author(s):

Yufeng Shi ◽

Xiaoli Zhao ◽

Shuang Gao ◽

Wenguan Ren ◽

Sipei Shao ◽

...

Keyword(s):

Magnetic Field ◽

High Precision ◽

Field Vector ◽

Magnetic Field Vector ◽

Coherent Superposition ◽

Vector Measurement ◽

Superposition States

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Optical measurement of non-uniform electric field vector distribution in a dielectric liquid using triplet measurement system

IEEE Transactions on Dielectrics and Electrical Insulation ◽

10.1109/94.983885 ◽

2002 ◽

Vol 9 (1) ◽

pp. 56-60 ◽

Cited By ~ 2

Author(s):

H. Ihori ◽

M. Fujii ◽

K. Arii

Keyword(s):

Electric Field ◽

Measurement System ◽

Optical Measurement ◽

Field Vector ◽

Dielectric Liquid ◽

Uniform Electric Field ◽

Electric Field Vector ◽

Vector Distribution

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Autonomous navigation algorithm based on AUKF filter about fusion of geomagnetic and sunlight directions

International Journal of Intelligent Computing and Cybernetics ◽

10.1108/ijicc-07-2017-0087 ◽

2018 ◽

Vol 11 (4) ◽

pp. 471-485 ◽

Cited By ~ 1

Author(s):

Bing Hua ◽

Zhiwen Zhang ◽

Yunhua Wu ◽

Zhiming Chen

Keyword(s):

Orbit Determination ◽

Geomagnetic Field ◽

Autonomous Navigation ◽

Unscented Kalman Filter ◽

Satellite Orbit ◽

Field Vector ◽

Low Earth Orbit ◽

Fusion Algorithm ◽

Content Type ◽

Navigation Algorithm

Purpose The geomagnetic field vector is a function of the satellite’s position. The position and speed of the satellite can be determined by comparing the geomagnetic field vector measured by on board three-axis magnetometer with the standard value of the international geomagnetic field. The geomagnetic model has the disadvantages of uncertainty, low precision and long-term variability. Therefore, accuracy of autonomous navigation using the magnetometer is low. The purpose of this paper is to use the geomagnetic and sunlight information fusion algorithm to improve the orbit accuracy. Design/methodology/approach In this paper, an autonomous navigation method for low earth orbit satellite is studied by fusing geomagnetic and solar energy information. The algorithm selects the cosine value of the angle between the solar light vector and the geomagnetic vector, and the geomagnetic field intensity as observation. The Adaptive Unscented Kalman Filter (AUKF) filter is used to estimate the speed and position of the satellite, and the simulation research is carried out. This paper also made the same study using the UKF filter for comparison with the AUKF filter. Findings The algorithm of adding the sun direction vector information improves the positioning accuracy compared with the simple geomagnetic navigation, and the convergence and stability of the filter are better. The navigation error does not accumulate with time and has engineering application value. It also can be seen that AUKF filtering accuracy is better than UKF filtering accuracy. Research limitations/implications Geomagnetic navigation is greatly affected by the accuracy of magnetometer. This paper does not consider the spacecraft’s environmental interference with magnetic sensors. Practical implications Magnetometers and solar sensors are common sensors for micro-satellites. Near-Earth satellite orbit has abundant geomagnetic field resources. Therefore, the algorithm will have higher engineering significance in the practical application of low orbit micro-satellites orbit determination. Originality/value This paper introduces a satellite autonomous navigation algorithm. The AUKF geomagnetic filter algorithm using sunlight information can obviously improve the navigation accuracy and meet the basic requirements of low orbit small satellite orbit determination.

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