Design and analysis of an orientation estimation system using coplanar gyro-free inertial measurement unit and magnetic sensors

Kao, C. F.; Chen, Tsung-Lin

doi:10.1016/j.sna.2008.02.008

Cited by 18 publications

(8 citation statements)

References 7 publications

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“…Data from accelerometers and magnetometers are typically used to determine orientation angles. In some tools, gyroscopes are employed to improve the low signal to noise ratios (SNR) of accelerometers and magnetometers [12]. A hybrid multi-sensor system that combines a magnetometer with a gyroscope may increase the accuracy of the azimuth measurement since a gyroscope's signal has low SNR and is robust against magnetic disturbances.…”

Section: Introductionmentioning

confidence: 99%

Data Fusion by a Supervised Learning Method for Orientation Estimation Using Multi-Sensor Configuration Under Conditions of Magnetic Distortion and Shock Impact

2020

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Accurate subsurface sensing during directional drilling is critical in the mining and energy extraction industries. One challenge is to measure the azimuth accurately. Azimuth measurements are hindered by magnetic disturbances such as iron debris, especially when magnetometers are used. Moreover, gyroscopes are susceptible to shocks during drilling surveys. To overcome these challenges, we developed a supervised learning filter (SLF) using a multi-sensor configuration (MSC) to accurately estimate the azimuth. The MSC consists of micro-electro-mechanical systems (MEMS) based magnetometers, gyroscopes, and accelerometers into two set of sensors, and the groups are separated by a known distance D to acquire additional rotational information using a dual acceleration difference (DAD) method. Also, D can reduce the negative effect of magnetic disturbances. A Kalman filter (KF) with known a priori noise information removes white noise; however, it is difficult to deal with unknown magnetic and shock disturbances. To reduce the effect of unknown magnetic and shock disturbances, we use the SLF to estimate orientation information. First, the SLF employs an adaptive neuro network fuzzy inference system (ANFIS) to build error models of each sensor; then the SLF calculates the proper weights of the sensors using the error models. Lab-scale experiments are performed on a test rig where the SLF is evaluated using one case with training and verified using two cases without training. The results showed an improvement in azimuth estimation.

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Section: Introductionmentioning

confidence: 99%

Data Fusion by a Supervised Learning Method for Orientation Estimation Using Multi-Sensor Configuration Under Conditions of Magnetic Distortion and Shock Impact

2020

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“…Literatürde, AÖB'lerde bulunan farklı algılayıcı kombinasyonlarının kullanıldığı birçok çalışma mevcuttur [17]- [29]. Sadece ivmeölçerin kullanıldığı çalışmalar [17], [24], [28]'de, sadece jiroskop kullanılan bir çalışma [18]'de, jiroskop-manyetometre çifti kullanılan bir çalışma [19]'da, ivmeölçer-manyetometre çifti kullanılan çalışma örnekleri [20], [21]'de sunulmuştur. Bu çalışmaların çoğunluğunda, açısal verilerden insan uzuvlarının konumu belirlenmeye çalışılmıştır.…”

Section: Introductionunclassified

Providing the human-robot interaction with biomimetic approach

Gelen¹,

Özcan²

2019

Pamukkale J Eng Sci

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Bu çalışmada, insan kol ve el hareketlerinin taklit edilmesiyle insanrobot etkileşimini sağlayan biyomimetik bir yaklaşım sunulmuştur. İnsan kol hareketleriyle robotun aynı doğrultuda hareket etmesi sağlanmış ve el hareketleri ile de robot tutucusunun kontrolü sağlanmıştır. Robot hareketi için; ilk olarak insan elinin, bel hizasında orijin noktası olarak belirlenen noktaya olan konumunu verecek kinematik model oluşturulmuştur. Modellemede, insan kolu, ön kol, pazı ve omuz olmak üzere üç ayrı uzuv olarak incelenmiştir. Omuza, pazıya ve ön kola yerleştirilen algılayıcılar ile dönüş açısı bilgileri elde edilmiş ve uzuv uzunlukları ile birlikte matematiksel modelde kullanılmıştır. Bu hesaplamalarda rotasyon kinematiği ve hareket kinematiği matrisleri kullanılmıştır. Tutucu kontrolü için ise bünyesinde EMG sensörleri bulunduran MYO kol bandı kullanılmıştır. Bu kol bandı üzerindeki EMG sensörleri ile kol kaslarından parmak hareketleri algılatılmıştır ve bu hareketler doğrultusunda pnömatik tutucu kontrol edilmiştir. Uygulamalarda 6-eksen robot kolu kullanılmıştır. Hesaplanan konum verileri ve tutucu bilgisi ethernet üzerinden TCP/IP protokolü ile robot denetleyicisine aktarılmaktadır. Robotun hesaplanan konuma gitmesini ve tutucu kontrolünü sağlayan kod oluşturularak robota aktarılmıştır. Yapılan testlerde, endüstriyel robotun insan kol ve el hareketleri ile başarılı biçimde kontrol edildiği gözlemlenmiştir. In this work, a biomimetic approach to provide human-robot interaction by mimicking the motion of human arm and fingers is presented. The movement of an industrial robot is performed by human arm movement in same direction and the control of gripper is also performed by hand movements. For the movement of robot, as a first step, a kinematic model is obtained to give the position of the human hand to the point determined as the origin point in the waist line. In the modelling, the human arm is considered as three limp that are forearm, biceps and shoulder. The rotational angles are obtained from sensors placed in the shoulder, biceps, and forearm, are used in the mathematical model with limb lengths. Rotation kinematics and kinematics matrices are used in these calculations. For the gripper control, a MYO armband with EMG sensors is used. With this EMG sensor on the armband, finger movements are detected from the arm muscles and the pneumatic gripper was controlled in the direction of these movements. A 6-axis robot arm is used in the applications. The calculated position data and the gripper information are transferred to the robot controller via the TCP/IP protocol over Ethernet. A code that provides reaching of robot to calculated position and control the gripper is created and transferred to robot. In the tests, it has been observed that the industrial robot has been successfully controlled by human arm and hand movements

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“…The MSAA approach was explored in the research of gyro-free strapdown inertial navigation systems and inertial measurement systems [5], [6], which senses the six-DOF acceleration by accessing and decoupling the outputs from the MSAA specifically positioned and oriented on a rigid body according to the appropriate configurations. The MSAA approach integrating six or more single-axis accelerometers has advantages of a simple research idea and simple structure.…”

Section: Introductionmentioning

confidence: 99%

Principle Research on a Single Mass Six-Degree-of-Freedom Accelerometer With Six Groups of Piezoelectric Sensing Elements

Lan

Liu

2015

IEEE Sensors J.

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This paper presents a novel six-degree-offreedom (six-DOF) accelerometer that based on single mass structure for determination of spatial vibration characteristics, which employs six groups of piezoelectric quartzes as acceleration sensing elements. The operating principle of the proposed six-DOF accelerometer is analyzed, and its analytical model and structure model are constructed. The numerical simulation model [finite-element model (FEM)] of the proposed six-DOF accelerometer is also established, and its output sensitivity, static characteristics, dynamic natural frequency, and so on are analyzed by FEM tools (ANSYS software). There is a consistency between the analytical model and the result of numerical simulation model. The research results show that the single mass six-DOF accelerometer with six groups of piezoelectric sensing elements can accurately measure six-DOF acceleration, and has advantages of simple and rational structure, correct sensing principle and mathematic model, good linearity, high stiffness, theoretical natural frequency is >10.2 kHz, and nice manufacturability.Index Terms-Six-DOF accelerometer, piezoelectric quartz, acceleration measurement, acceleration transducer. 1530-437X

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Design and analysis of an orientation estimation system using coplanar gyro-free inertial measurement unit and magnetic sensors

Cited by 18 publications

References 7 publications

Data Fusion by a Supervised Learning Method for Orientation Estimation Using Multi-Sensor Configuration Under Conditions of Magnetic Distortion and Shock Impact

Data Fusion by a Supervised Learning Method for Orientation Estimation Using Multi-Sensor Configuration Under Conditions of Magnetic Distortion and Shock Impact

Providing the human-robot interaction with biomimetic approach

Principle Research on a Single Mass Six-Degree-of-Freedom Accelerometer With Six Groups of Piezoelectric Sensing Elements

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