Presence Of Magnetic Disturbances Research Articles

3-D Electromagnetic Position Estimation System Using High-Magnetic-Permeability Metal for Continuum Medical Robots

In this letter, a new 3-D electromagnetic position sensing method is proposed for localization of continuum medical robots. An electromagnet and magnetic sensors are placed outside the human body while only a piece of passive mu-metal with high magnetic permeability is attached to the robot moving inside the body, resulting in a wireless non-contacting position estimation system. The mu-metal gets easily magnetized by the electromagnet and thus exerts position-dependent influence on the external magnetic field, which is measured for position estimation using a particle filter. An alternating magnetic field from the electromagnet is used and hence disturbances from nearby ferromagnetic objects can be rejected. The 3-D position estimation system is evaluated on a flexible trans-esophageal robot for ultrasound imaging with motions of insertion and maneuver. Experiments show that the mean position estimation error is about 5 mm and the system is robust in the presence of magnetic disturbances from a ferromagnetic object. This new wireless and robust 3-D position estimation system is demonstrated to have the potential to localize a continuum medical robot, which can enable autonomous navigation of the robot.

Sparse Magnetometer-free Inertial Motion Tracking – A Condition for Observability in Double Hinge Joint Systems

Inertial measurement units are commonly used in a growing number of application fields to track or capture motions of kinematic chains, such as human limbs, exoskeletons or robotic actuators. A major challenge is the presence of magnetic disturbances that results in unreliable magnetometer readings. Recent research revealed that this problem can be overcome by exploitation of kinematic constraints. While typically each segment of the kinematic chain is equipped with an IMU, a novel approach called sparse inertial motion tracking aims at inferring the complete motion states from measurements of a reduced set of sensors. In the present contribution, we combine the magnetometer-free and the sparse approach for real-time motion tracking of double-hinge joint systems with non-parallel joint axes. Analyzing the observability of the system, we find a condition which assures that the relative orientations between all segments are uniquely determined by a kinematic constraint, which contains only the gyroscope readings. Furthermore, we propose a moving-horizon estimator and validate it in a simulation study of four movements, which differ by their degrees of excitation. The results of this study confirm the theoretical conjectures and demonstrate that magnetometer-free sparse inertial real-time motion tracking is feasible under precise and simple excitation conditions.

An Adaptive Orientation Estimation Method for Magnetic and Inertial Sensors in the Presence of Magnetic Disturbances.

Magnetic and inertial sensors have been widely used to estimate the orientation of human segments due to their low cost, compact size and light weight. However, the accuracy of the estimated orientation is easily affected by external factors, especially when the sensor is used in an environment with magnetic disturbances. In this paper, we propose an adaptive method to improve the accuracy of orientation estimations in the presence of magnetic disturbances. The method is based on existing gradient descent algorithms, and it is performed prior to sensor fusion algorithms. The proposed method includes stationary state detection and magnetic disturbance severity determination. The stationary state detection makes this method immune to magnetic disturbances in stationary state, while the magnetic disturbance severity determination helps to determine the credibility of magnetometer data under dynamic conditions, so as to mitigate the negative effect of the magnetic disturbances. The proposed method was validated through experiments performed on a customized three-axis instrumented gimbal with known orientations. The error of the proposed method and the original gradient descent algorithms were calculated and compared. Experimental results demonstrate that in stationary state, the proposed method is completely immune to magnetic disturbances, and in dynamic conditions, the error caused by magnetic disturbance is reduced by 51.2% compared with original MIMU gradient descent algorithm.

A novel robust Kalman filter on AHRS in the magnetic distortion environment

This paper deals with the Attitude and Heading Reference System (AHRS) under the conditions of magnetic distortion. The conventional AHRS involves monitoring the difference between the measurements and the projections of gravity reference vector and geomagnetic reference vector in body coordinate (Right-Forward-Up). However, due to the magnetic distortion, the attitude estimated by AHRS will suffer from a fatal error. To solve this problem, we propose an indirect Kalman filter with two-step observations update, which makes the magnetic disturbance only influence the output of heading and not affect the estimated pitch and roll angles. In addition, the proposed system performs online detection of magnetic disturbances by exploiting variations in magnetic dip angle. According to the result of detection, we use the equivalent weight matrix to place more emphasis on the results of dead reckoning of the gyroscope measurements and less on the magnetic observation update. The experiment validation shows that the proposed system has an accurate and timely detection and has more robust performance than standard Kalman filter in the presence of magnetic disturbances.

First Report on Seismogenic Magnetic Disturbances over Brazilian Sector

In this work, we present a first report on magnetic disturbances over the Brazilian sector that occurred on September 16–17, 2015. These disturbances are associated with the Illapel earthquake. Using seven magnetometers installed across Brazil, and distributed within 4500 km from the epicenter, the presence of magnetic disturbances is noted ~15 min after the time of earthquake. The seismometers registered the arrival of the first seismic waves ~10 min prior to the observed signatures on magnetic disturbances, suggesting that such waves are possibly from seismogenic origin, which involves the lithosphere–atmosphere–ionosphere coupling.

A new magnetic compass calibration algorithm using neural networks

The magnetic compass can provide heading direction by measuring the Earth's magnetic field. In practical applications, there usually exists an unwanted local magnetic field that will distort the magnetometer measurements; hence a calibration procedure is essential. Current calibration methods are limited by the inaccurate magnetometer error estimation when measurements are deteriorated by magnetic disturbances or large noises. This paper proposes a new compass calibration algorithm via modelling the nonlinear relationship between the compass heading and the true heading using neural networks. When an external heading reference is available, neural networks can be trained to properly model this nonlinear input–output pattern even in the presence of magnetic disturbances, and subsequently can be applied to convert the compass heading into the correct heading. The proposed algorithm does not require declination information and magnetometer biases and scale factor estimation. The simulation and field test results have verified the effectiveness and robustness of the proposed calibration method and have also shown that the calibration performance is proportional to the quality of the training data.