Formulation of a new gradient descent MARG orientation algorithm: Case study on robot teleoperation

Wilson, Samuel; Eberle, Henry; Hayashi, Yoshikatsu; Madgwick, Sebastian; McGregor, Alison H.; Jing, Xingjian; Vaidyanathan, Ravi

doi:10.1016/j.ymssp.2019.04.064

Cited by 66 publications

(37 citation statements)

References 50 publications

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“…The microelectromechanical systems-based (MEMS-based) relative localization problem is a recent topic, which has been widely investigated in many areas including robotics and control [1][2][3][4][5][6][7][8], healthcare and rehabilitation [9][10][11], consumer electronics mobile devices [12][13][14], and automated driving and navigation [15][16][17][18], both in industry and in scientific research. Independent from the application, accurate and robust attitude estimation is a crucial task to be solved, especially if the results are to be incorporated into unstable closed-loop systems, such as the control algorithms of mobile robots and unmanned aerial vehicles (UAVs) [1].…”

Section: Survey On Attitude Estimationmentioning

confidence: 99%

“…where δ kl denotes the Kronecker delta. Gyroscope-based (gyro-based) attitude realization is obtained by numerical integration of the true angular velocity vector ω k in Equation (7). Common calibration procedures performed in laboratories allow for the determination and compensation of the scale factor and misalignment errors.…”

Section: Gyroscope Modelmentioning

confidence: 99%

“…The popular Madgwick filter [26] is a computationally efficient constant gain filter, which was developed originally for human motion tracking applications. The filter has been improved recently in [7], employing the accelerometer and magnetometer measurements in a gradient descent algorithm to correct the quaternion obtained through the integration of rate measurements. Mahony and Madgwick filters are widely utilized algorithms and their performances have regularly been considered in comparative Sensors 2020, 20, 803 3 of 29 analyses [9,13,15,[31][32][33].…”

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confidence: 99%

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A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

Odry

Kecskés

Šarčević

et al. 2020

Sensors

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This paper proposes a novel fuzzy-adaptive extended Kalman filter (FAEKF) for the real-time attitude estimation of agile mobile platforms equipped with magnetic, angular rate, and gravity (MARG) sensor arrays. The filter structure employs both a quaternion-based EKF and an adaptive extension, in which novel measurement methods are used to calculate the magnitudes of system vibrations, external accelerations, and magnetic distortions. These magnitudes, as external disturbances, are incorporated into a sophisticated fuzzy inference machine, which executes fuzzy IF-THEN rules-based adaption laws to consistently modify the noise covariance matrices of the filter, thereby providing accurate and robust attitude results. A six-degrees of freedom (6 DOF) test bench is designed for filter performance evaluation, which executes various dynamic behaviors and enables measurement of the true attitude angles (ground truth) along with the raw MARG sensor data. The tuning of filter parameters is performed with numerical optimization based on the collected measurements from the test environment. A comprehensive analysis highlights that the proposed adaptive strategy significantly improves the attitude estimation quality. Moreover, the filter structure successfully rejects the effects of both slow and fast external perturbations. The FAEKF can be applied to any mobile system in which attitude estimation is necessary for localization and external disturbances greatly influence the filter accuracy.The MEMS inertial measurement unit (IMU), composed of tri-axis MEMS accelerometer, gyroscope, and magnetometer sensors, also known as the measurement system of magnetic, angular rate, and gravity (MARG) sensor arrays, is the most commonly utilized device to track the real-time orientation of mobile platforms at present. The low-cost, low power consumption, and small size characteristics meet technological requirements, and therefore these devices have been widely utilized in embedded systems, where the filtering algorithm is executed by a microprocessor. As a result, an attitude and heading reference system (AHRS) has been formed, which provides the complete orientation measurement relative to the Earth's gravitational and magnetic fields (global reference system), where the attitude denotes the roll and pitch angles, whereas heading refers to the yaw Euler angle [19]. The role of the aforementioned filtering algorithm is to combine the individual features of each sensor and provide both properly smoothed and robust attitude results with regard to the global reference system, in either Euler angles or quaternions. The most common method applied in sensor fusion techniques synthesizes the short-term accuracy of gyroscope-based attitude realizations and the accelerometer and magnetometer provide rough, low-frequency attitude corrections. This technique cancels the accumulated error (drift), smooths the signals, and produces long-term stable outputs if the IMU is in stationary states. Significant decrease in estimation performance arises when...

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Section: Survey On Attitude Estimationmentioning

confidence: 99%

Section: Gyroscope Modelmentioning

confidence: 99%

mentioning

confidence: 99%

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A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

Odry

Kecskés

Šarčević

et al. 2020

Sensors

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show abstract

“…Optimization filters derive orientation by estimating a vector representing the sensor output at the current orientation and minimizing the difference between predicted and observed output [12]. These techniques have shown acceptable accuracy with lower computational expense than Kalman filters.…”

Section: A Overview Of Imu Orientation Estimation Algorithmsmentioning

confidence: 99%

“…The GDA is well acknowledged as one of the most popular inertial sensor fusion algorithms today [18], [29] with a wealth of literature comparing its performance to available alternatives. It is commonly used to benchmark inertial sensor fusion (e.g., [3], [12], [32], [34]). It was selected as a benchmark for comparison to maintain consistency with testing procedures in existing literature.…”

Section: Ecf-gda Performance Benchmarkingmentioning

confidence: 99%

An Extended Complementary Filter for Full-Body MARG Orientation Estimation

Madgwick¹,

Wilson

Turk

et al. 2020

IEEE/ASME Trans. Mechatron.

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Inertial sensing suites now permeate all forms of smart automation, yet a plateau exists in the real-world derivation of global orientation. Magnetic field fluctuations and inefficient sensor fusion still inhibit deployment. In this article, we introduce a new algorithm, an extended complementary filter (ECF), to derive 3-D rigid body orientation from inertial sensing suites addressing these challenges. The ECF combines computational efficiency of classic complementary filters with improved accuracy compared to popular optimization filters. We present a complete formulation of the algorithm, including an extension to address the challenge of orientation accuracy in the presence of fluctuating magnetic fields. Performance is tested under a variety of conditions and benchmarked against the commonly used gradient decent inertial sensor fusion algorithm. Results demonstrate improved efficiency, with the ECF achieving convergence 30% faster than standard alternatives. We further demonstrate an improved robustness to sources of magnetic interference in pitch and roll and to fast changes of orientation in the yaw direction. The ECF has been implemented at the core of a wearable rehabilitation system tracking movement of stroke patients for home telehealth. The ECF and accompanying magnetic disturbance rejection algorithm enables previously unachievable real-time patient movement feedback in the form of a full virtual human (avatar), even in the presence of magnetic Manuscript

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Adaptive neural network based compensation control of quadrotor for robust trajectory tracking

Bouaiss

Mechgoug

Taleb‐Ahmed

et al. 2023

Adaptive Control & Signal

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SummaryNeural network (NN) methods have become increasingly efficient and applicable in control. This article presents a novel nested control strategy based on adaptive radial basis function neural networks (RBFNN) and NN supervised control embedded with integrator back stepping (IBS) for a robust position and attitude trajectory tracking of quadrotor aerial robot in presence of modeling uncertainties, sensing noise, and external bounded disturbance. The control philosophy is derived from the decentralized inverse dynamics, where the adaptive RBFNN is adopted for the outer loop to approximate the unknown external disturbance, and adaptively compensate for their effect. In conjunction with a supervised control that stabilizes the attitude in the inner loop by adaptively compensating the unknown and unmodeled uncertainties, avoiding initial instability of the attitude during NN convergence. Furthermore, noise is attenuated by an adaptive extended Kalman filter. Stability analysis was elaborated using Lyapunov stability theory. Simulation of adaptive RBFNN control philosophy proved robustness and effectiveness compared to proportional integral derivative, IBS, and offline decentralized convolution neural network algorithms as it generates the control law by guarantying a fast convergence of parameters, better external disturbance compensation, and noise attenuation. The proposed scheme of robust tracking was validated.

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Formulation of a new gradient descent MARG orientation algorithm: Case study on robot teleoperation

Cited by 66 publications

References 50 publications

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

An Extended Complementary Filter for Full-Body MARG Orientation Estimation

Adaptive neural network based compensation control of quadrotor for robust trajectory tracking

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