Euler Angle Based Attitude Estimation Avoiding the Singularity Problem

Kang, Chul Woo; Park, Chan Gook

doi:10.3182/20110828-6-it-1002.01993

Cited by 10 publications

(4 citation statements)

References 4 publications

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“…In the second step, the EKF estimates yaw using, at the prediction stage, the gyro data and the estimated pitch and roll and, at the correction stage, the magnetometer data. The definition of a reduced system state (as in [16,17,18]) at the first EKF achieves: (1) The system model depends only on the pitch and roll angle and it is independent of the yaw angle; (2) the system model is easily linearized; and (3), in the measurement model, the accelerometer output is used directly without further complex transformations. Unlike [17], at the second EKF, the state of the system includes gyro bias to solve the problem of estimation errors related to gyro bias.…”

Section: Discussionmentioning

confidence: 99%

“…System state is defined [16,17,18] by using the third column of the director cosines matrix (Equation (1)), so boldx=[]x1x2x3=[]−sinθsinϕcosθcosϕcosθ…”

Section: Methodsmentioning

confidence: 99%

“…At the observation stage, the information provided by the magnetometer will be used to compute an estimation for ψ. Again, it will be necessary to make a transformation for the vector that points to the magnetic north Hn=false[HNHEHDfalse]T, taken at the fixed frame, with respect to body frame Hb by means of Hb=EnbHn where Enb is the matrix of Equation (1) and so the resulting matrix is [17,18]:Hb=[]HNcosθcosψ+HEcosθsinψ−HDsinθHNfalse(prefixsinϕprefixsinθprefixcosψ−prefixcosϕprefixsinψfalse)+HEfalse(prefixsinϕprefixsinθprefixsinψ+prefixcosϕprefixcosψfalse)+HDsinϕcosθHNfalse(prefixcosϕprefixsinθprefixcosψ+prefixsin<...>…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

HUSP: A Smart Haptic Probe for Reliable Training in Musculoskeletal Evaluation Using Motion Sensors

Moreno

Curto

García-Esteban

et al. 2018

Sensors

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As a consequence of the huge development of IMU (Inertial Measurement Unit) sensors based on MEMS (Micro-Electromechanical Systems), innovative applications related to the analysis of human motion are now possible. In this paper, we present one of these applications: a portable platform for training in Ultrasound Imaging-based musculoskeletal (MSK) exploration in rehabilitation settings. Ultrasound Imaging (USI) in the diagnostic and treatment of MSK pathologies offers various advantages, but it is a strongly operator-dependent technique, so training and experience become of fundamental relevance for rehabilitation specialists. The key element of our platform is a replica of a real transducer (HUSP—Haptic US Probe), equipped with MEMS based IMU sensors, an embedded computing board to calculate its 3D orientation and a mouse board to obtain its relative position in the 2D plane. The sensor fusion algorithm used to resolve in real-time the 3D orientation (roll, pitch and yaw angles) of the probe from accelerometer, gyroscope and magnetometer data will be presented. Thanks to the results obtained, the integration of the probe into the learning platform allows a haptic sensation to be recreated in the rehabilitation trainee, with an attractive performance/cost ratio.

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Section: Discussionmentioning

confidence: 99%

“…System state is defined [16,17,18] by using the third column of the director cosines matrix (Equation (1)), so boldx=[]x1x2x3=[]−sinθsinϕcosθcosϕcosθ…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

HUSP: A Smart Haptic Probe for Reliable Training in Musculoskeletal Evaluation Using Motion Sensors

Moreno

Curto

García-Esteban

et al. 2018

Sensors

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“…Both the left and right trunk segments have IMUs to measure the 3-axis angular velocities and accelerations of the body. These signals are fed to a trunk orientation estimator [ 29 ] to calculate the roll and pitch orientations of the frame. After determining the orientation of the trunk segments, the robot infers the pitch orientation of each lower limb segment.…”

Section: Methodsmentioning

confidence: 99%

A Neural Network-Based Gait Phase Classification Method Using Sensors Equipped on Lower Limb Exoskeleton Robots

Jung

Heo

Yang

et al. 2015

Sensors

107

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An exact classification of different gait phases is essential to enable the control of exoskeleton robots and detect the intentions of users. We propose a gait phase classification method based on neural networks using sensor signals from lower limb exoskeleton robots. In such robots, foot sensors with force sensing registers are commonly used to classify gait phases. We describe classifiers that use the orientation of each lower limb segment and the angular velocities of the joints to output the current gait phase. Experiments to obtain the input signals and desired outputs for the learning and validation process are conducted, and two neural network methods (a multilayer perceptron and nonlinear autoregressive with external inputs (NARX)) are used to develop an optimal classifier. Offline and online evaluations using four criteria are used to compare the performance of the classifiers. The proposed NARX-based method exhibits sufficiently good performance to replace foot sensors as a means of classifying gait phases.

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Developing of Strapdown Inertial Attitude System

Sapehin

2023

Studies in Systems, Decision and Control

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Euler Angle Based Attitude Estimation Avoiding the Singularity Problem

Cited by 10 publications

References 4 publications

HUSP: A Smart Haptic Probe for Reliable Training in Musculoskeletal Evaluation Using Motion Sensors

HUSP: A Smart Haptic Probe for Reliable Training in Musculoskeletal Evaluation Using Motion Sensors

A Neural Network-Based Gait Phase Classification Method Using Sensors Equipped on Lower Limb Exoskeleton Robots

Developing of Strapdown Inertial Attitude System

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