• International Journal of Ocean System Engineering
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• v.1 no.3
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• pp.165-170
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• 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 Electrical Engineering and Technology
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• v.12 no.4
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• pp.1675-1682
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• 2017

This paper deals with three dimensional orientation estimation algorithm for an attitude and heading reference system (AHRS) based on nine-axis inertial/magnetic sensor signals. In terms of the orientation estimation based on the use of a Kalman filter (KF), the quaternion is arguably the most popular orientation representation. However, one critical drawback in the quaternion representation is that undesirable magnetic disturbances affect not only yaw estimation but also roll and pitch estimations. In this paper, a sequential direction cosine matrix-based orientation KF for AHRS has been presented. The proposed algorithm uses two linear KFs, consisting of an attitude KF followed by a heading KF. In the latter, the direction of the local magnetic field vector is projected onto the heading axis of the inertial frame by considering the dip angle, which can be determined after the attitude KF. Owing to the sequential KF structure, the effects of even extreme magnetic disturbances are limited to the roll and pitch estimations, without any additional decoupling process. This overcomes an inherent issue in quaternion-based estimation algorithms. Validation test results show that the proposed method outperforms other comparison methods in terms of the yaw estimation accuracy during perturbations and in terms of the recovery speed.

• International Journal of Ocean System Engineering
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• v.2 no.4
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• pp.223-232
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• 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 Advanced Navigation Technology
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• v.15 no.2
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• pp.181-188
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• 2011

A small UAVs(Unmaned Aerial Vehicles) have limited by the payload capacity which requires miniaturization of a navigation system. In this paper, the performance of the lightweight and small sized AHRS(Attitude Heading Reference System), which is self-developed, is evaluated at low acceleration environment. The designed AHRS adopts the commercial low-cost MEMS sensors. A quaternion-based attitude calculation method, which eliminates singularity with relatively simple algebra, is used. In an attitude correction algorithm, the Kalman filter is used with accelerometers and magnetometers combined. The fabricated AHRS is also evaluated with reference to a COTS(Commercial Off-The-Shelf) AHRS which reports a number of successful applications to a small UAVs. The test results show that the measurements from the fabricated AHRS provide proper attitude output data with acceptable amount of differences(horizontal axis 0.5$^{\circ}$, vertical axis 1.5$^{\circ}$) in test environment.

• Journal of the Korean Society of Marine Environment & Safety
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• v.19 no.6
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• pp.666-673
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• 2013

This paper proposes the performance evaluation test of attitude heading reference system (AHRS) suitable for small high speed autonomous underwater vehicle(AUV). Although IMU can provides the detail attitude information, it is sometime not suitable for small AUV with short operation time in view of price and the electrical power consumption. One of alternative for tactical grade IMU is the AHRS based micro-machined electro mechanical system(MEMS) which can overcome many problems that have inhibited the adoption of inertial system for small AUV such as cost and power consumption. A cost effective and small size AHRS which incorporates measurements from 3-axis MEMS gyroscopes, accelerometers, and 3-axis magnetometers has been developed to provide a complete attitude solution for AUV and the attitude calculation algorithm is derived based the coordinate transform equation and Kalman filter. The developed AHRS was validated through various performance tests as like the magnetometer calibration, operating experiments using land mobile vehicle and flight motion simulator (FMS). The test of magnetometer calibration shows the developed MEMS AHRS is robust to the external magent field change and the test with land vehicle proves the leveling error of developed MEMS AHRS is below $0.5^{\circ}/hr$. The results of FMS test shows the fact that AHRS provides the measurement with $0.5^{\circ}/hr$ error during 5 minutes operation time. These results of performance evaluation tests showed that the developed AHRS provides attitude information which error of roll and pitch are below $1^{\circ}$ and the error of yaw is below $5^{\circ}$ and satisfies the required specification. It is expected that developed AHRS can provide the precise attitude measurement under sea trial with real AUV.

• Journal of Platform Technology
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• v.6 no.4
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• pp.41-48
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• 2018

Accidents involving infants and toddlers are occurring frequently, and if elementary measures are not smooth, they can lead to even greater accidents. Rapid elementary measures require an appropriate monitoring system, and it is necessary to build a system that can accurately monitor the child's living condition and notify the emergency situation immediately. Recently, with the development of IoT technology, various types of sensors are being developed in a very small size, and development of wearable devices connected with smart phones is accelerating with the development of smart phones. By using these wearable devices, it is possible to reduce the risk of infant accidents and to respond promptly in case of an accident. In this paper, we propose a wearable system that can monitor the infant condition and present a service to prevent infant accidents.

• Journal of Ocean Engineering and Technology
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• v.26 no.5
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• pp.81-86
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• 2012

Unmanned underwater vehicles have many applications in scientific, military, and commercial areas because of their autonomy. In many cases, an underwater vehicle adopts a control algorithm based on a tactical inertial sensor for precise control. However, a control algorithm that uses a tactical inertial sensor is unsuitable for some underwater vehicle missions such as torpedo decoys. This paper proposes a control algorithm for an unmanned underwater vehicle that does not require precise control. The control algorithm proposed for an unmanned underwater vehicle adopts a low cost MEMS inertial sensor, and simulations using the specifications of the MEMS inertial sensor under development are performed to verify the control algorithm under a real environment. The results of these simulations are presented.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.3
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• pp.555-564
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• 2017

In this paper, a new technique for attitude and heading reference system (AHRS) using low-cost MEMS sensors of the gyroscope, accelerometer, and magnetometer is addressed particularly in vibration environments. The motion of MEMS sensors interact with the scale factor and cross-coupling errors to produce random errors by the harsh environment. A new adaptive attitude estimation algorithm based on the Kalman filter is developed to overcome these undesirable side effects by analyzing windowed measurement error covariance. The key idea is that performance degradation of accelerometers, for example, due to linear vibrations can be reduced by the proposed measurement error covariance analysis. The computed error covariance is utilized to the measurement covariance of Kalman filters adaptively. Finally, the proposed approach is verified by using numerical simulations and experiments in an acceleration phase and/or vibrating environments.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.4
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• pp.349-355
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• 2015

Underwater robots generally show better performances for tasks than humans under certain underwater constraints such as. high pressure, limited light, etc. To properly diagnose in an underwater environment using remotely operated underwater vehicles, it is important to keep autonomously its own position and orientation in order to avoid additional control efforts. In this paper, we propose an efficient method to assist in the operation for the various disturbances of a remotely operated vehicle for the diagnosis of underwater structures. The conventional AHRS-based bearing estimation system did not work well due to incorrect measurements caused by the hard-iron effect when the robot is approaching a ferromagnetic structure. To overcome this drawback, we propose a sensor fusion algorithm with the camera and AHRS for estimating the pose of the ROV. However, the image information in the underwater environment is often unreliable and blurred by turbidity or suspended solids. Thus, we suggest an efficient method for fusing the vision sensor and the AHRS with a criterion which is the amount of blur in the image. To evaluate the amount of blur, we adopt two methods: one is the quantification of high frequency components using the power spectrum density analysis of 2D discrete Fourier transformed image, and the other is identifying the blur parameter based on cepstrum analysis. We evaluate the performance of the robustness of the visual odometry and blur estimation methods according to the change of light and distance. We verify that the blur estimation method based on cepstrum analysis shows a better performance through the experiments.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.12
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• pp.1124-1131
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• 2015

This paper describes a 5-dof ground testbed which emulates micro-gravity environment for developing Rendezvous/docking algorithm of a nano-satellite. The testbed consists of two parts, the low part which eliminates friction force with ground and the upper part which has 3-dof rotational motion with respect to the low part. For Vison-based autonomous navigation algorithm, we use camera, LIDAR and AHRS as sensors and eight cold gas thrusters and three axis directional reaction wheels as actuators. All system software are implemented with C++ based on on-board computer and Linux OS.