• Journal of Korea Society of Industrial Information Systems
• /
• v.18 no.4
• /
• pp.37-41
• /
• 2013

A balance maintain system of Stewart platform using AHRS(Attitude and Heading Reference System) sensor is introduced. The Stewart platform is used for controlling a standing plate to keep up horizontal level at any slopes. To know current leaned degrees, AHRS sensor is used. We made feed-back system that AHRS sensor sends current status and the Stewart platform revises top plate to be equilibrium state.

• International Journal of Ocean System Engineering
• /
• v.1 no.3
• /
• pp.165-170
• /
• 2011

In this paper, for the accurate estimation of the position and orientation of the UUV (unmanned underwater vehicle), an AHRS (Attitude Heading Reference System) was developed using the IMU (inertial measurement unit) sensor which provides information on acceleration and orientation in the object coordinate and the initial alignment algorithm and the E-KF (extended Kalman Filter). The initial position and orientation of the UUV are estimated using the initial alignment algorithm with 3-axis acceleration and geomagnetic information of the IMU sensor. The position and orientation of the UUV are estimated using the AHRS composed of 3-axis acceleration, velocity, and geomagnetic information and the E-KF. For the performance test of the orientation estimation of the AHRS, a testbed using IMU sensor(ADIS16405) and DSP28335 coded with an E-KF algorithm was developed and its performance was verified through tests.

• Journal of Advanced Marine Engineering and Technology
• /
• v.39 no.5
• /
• pp.522-526
• /
• 2015

Generally, underwater unmanned vehicle have adopted an inertial navigation system (INS), dead reckoning (DR), acoustic navigation and geophysical navigation techniques as the navigation method because GPS does not work in deep underwater environment. Even if the tactical inertial sensor can provide very detail measurement during long operation time, it is not suitable to use the tactical inertial sensor for small size and low cost UUV because the tactical inertial sensor is expensive and large. One alternative to INS is attitude heading reference system (AHRS) with the micro-machined electro mechanical system (MEMS) inertial sensor because of MEMS inertial sensor's small size and low power requirement. A cost effective and small size attitude heading reference system (AHRS) which incorporates measurements from 3-axis micro-machined electro mechanical system (MEMS) gyroscopes, accelerometers, and 3-axis magnetometers has been developed to provide a complete attitude solution for UUV. The AHRS based MEMS overcome many problems that have inhibited the adoption of inertial system for small UUV such as cost, size and power consumption. Several evaluation experiments were carried out for the validation of the developed AHRS's function and these experiments results are presented. Experiments results prove the fact that the developed MEMS AHRS satisfied the required specification.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.7 no.1
• /
• pp.177-186
• /
• 2017

Recently, Autonomous vehicles are getting hot attention. Amazon, the biggest online shopping service provider is developing a delivery system that uses drones. This kinds of platforms are need accurate attitude information for navigation. In this paper, a structure design of AHRS using low-cost inertia sensor is proposed. To estimate attitudes a Kalman filter which uses a quaternion based dynamic model, bias-removed measurements from MEMS Gyro, raw measurements from MEMS accelerometer and magnetometer, is designed. To remove bias from MEMS Gyro, an additional Kalman filter which uses raw Gyro measurements and attitude estimates, is designed. The performance of implemented AHRS is compared with high price off-the-shelf 3DM-GX3-25 AHRS from Microstrain. The Gyro bias was estimated within 0.0001[deg/s]. And from the estimated attitude, roll and pitch angle error is smaller than 0.2 and 0.3 degree. Yaw angle error is smaller than 6 degree.

• Journal of the Korea Society of Computer and Information
• /
• v.20 no.3
• /
• pp.89-96
• /
• 2015

IT is widely used for biomechanics and AHRS sensor also be highlighted with small sized characteristics and price competitiveness in the field of motion measurement and analysis of sports. In this paper, we attach the AHRS to the figure skate shoes to measure the motion data like spin, forward/backward, jump, in/out edge and toe movement. In order to reduce the measurement error, we have adopted the sensors equipped with Madgwick complementary filtering and also use Euler angle to quaternion conversion to reduce the Gimbal-lock effect. We test and experiment the accuracy and execution time of the pattern recognition algorithms like PCA, ICA, LDA, SVM to show the recognition possibility of it on the basic motions of figure skating from the 9-axis trajectory information which is gathered from AHRS sensor. From the result, PCA, ICA have low accuracy, but LDA, SVM have good accuracy to use for recognition of basic motions of figure skating.

• International Journal of Ocean System Engineering
• /
• v.2 no.4
• /
• pp.223-232
• /
• 2012

For the accurate estimation of the position and orientation of a UUV (unmanned underwater vehicle), a low-cost AHRS (attitude heading reference system) was developed using a low-cost IMU (inertial measurement unit) sensor which provides information on the 3D acceleration, 3D turning rate and 3D earth-magnetic field data in the object coordinate system. The main hardware system is composed of an IMU sensor (ADIS16405) and TMS320F28335, which is coded with an extended kalman filter algorithm with a 50-Hz sampling frequency. Through an experimental gimbal device, good estimation performance for the pitch, roll, and yaw angles of the developed AHRS was verified by comparing to those of a commercial AHRS called the MTi system. The experimental results are here presented and analyzed.

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.12
• /
• pp.1167-1172
• /
• 2015

This paper describes the implementation of wearable AHRS for an electromagnetic motion capture system that can trace and analyze human motion on the principal nine axes of inertial sensors. The module provides a three-dimensional (3D) attitude and heading angles combining MEMS gyroscopes, accelerometers, and magnetometers based on the extended Kalman filter, and transmits the motion data to the 3D simulation via Wi-Fi to realize the unrestrained movement in open spaces. In particular, the accelerometer in AHRS is supposed to measure only the acceleration of gravity, but when a sensor moves with an external linear acceleration, the estimated linear acceleration could compensate the accelerometer data in order to improve the precision of measuring gravity direction. In addition, when an AHRS is attached in an arbitrary position of the human body, the compensation of the axis of rotation could improve the accuracy of the motion capture system.

• Journal of Sensor Science and Technology
• /
• v.27 no.2
• /
• pp.86-92
• /
• 2018

In recent times, motion capture technology using inertial measurement unit (IMU) sensors has been actively used in sports. In this study, we developed a canoe paddle, installed with an IMU and a water level sensor, as a system tool for training and calibration purposes in water sports. The hardware was fabricated to control an attitude heading reference system (AHRS) module, a water level sensor, a communication module, and a wireless charging circuit. We also developed an application program for the mobile device that processes paddling motion data from the paddling operation and also visualizes it. An AHRS module with acceleration, gyro, and geomagnetic sensors each having three axes, and a resistive water level sensor that senses the immersion depth in the water of the paddle represented the paddle motion. The motion data transmitted from the paddle device is internally decoded and classified by the application program in the mobile device to perform visualization and to operate functions of the mobile training/correction system. To conclude, we tried to provide mobile knowledge service through paddle sport data using this technique. The developed system works reasonably well to be used as a basic training and posture correction tool for paddle sports; the transmission delay time of the sensor system is measured within 90 ms, and it shows that there is no complication in its practical usage.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.3
• /
• pp.83-89
• /
• 2016

Internet of Things (IoT) is widely used for biomechanics in sports activities and AHRS(Attitude and Heading Reference System) is a more cost effective solution than conventional high-grade IMUs (Inertial Measurement Units) that only integrate gyroscopes. In this paper, we attach the AHRS to the snowboard to measure the motion data like Air To Fakie, Caballerial and Free Style. In order to reduce the measurement error, we have adopted the sensors equipped with Kalman filtering and also used Euler angle to quaternion conversion to reduce the Gimbal-lock effect. We have tested and evaluated the accuracy and execution time of the pattern recognition algorithms like PCA, ICA, LDA, SVM to show the recognition possibility of it on the basic motions of Snowboarding from the 9-axis trajectory information which is gathered from AHRS sensor. With the result, PCA, ICA have low accuracy, but SVM have good accuracy to use for recognition of basic motions of Snowboarding.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.12
• /
• pp.2365-2370
• /
• 2017

In this paper, we propose a motion and position recognition technique considering an experiential VR environment. Motion recognition attaches a plurality of AHRS devices to a body part and defines a coordinate system based on this. Based on the 9 axis motion information measured from each AHRS device, the user's motion is recognized and the motion angle is corrected by extracting the joint angle between the body segments. The location recognition extracts the walking information from the inertial sensor of the AHRS device, recognizes the relative position, and corrects the cumulative error using the BLE fingerprint. To realize the proposed motion and position recognition technique, AHRS-based position recognition and joint angle extraction test were performed. The average error of the position recognition test was 0.25m and the average error of the joint angle extraction test was $3.2^{\circ}$.