Development and Testing of a Self-Contained, Portable Instrumentation System for a Fighter Pilot Helmet

A self-contained, portable, inertial and positional measurement system was developed and tested for an HGU-55 model fighter pilot helmet. The system, designated the Portable Helmet Instrumentation System (PHIS), demonstrated the recording of accelerations and rotational rates experienced by the human head in a flight environment. A compact, self-contained, “knee-board” sized computer recorded these accelerations and rotational rates during flight. The present research presents the results of a limited evaluation of this helmet-mounted instrumentation system flown in an Extra 300 fully aerobatic aircraft. The accuracy of the helmet-mounted, inertial head tracker system was compared to the aircraft-mounted referenced system. The ability of the Portable Helmet Instrumentation System to record position, orientation and inertial information in ground and flight conditions was evaluated. The capability of the Portable Helmet Instrumentation System to provide position, orientation and inertial information with sufficient fidelity was evaluated. The concepts demonstrated in this system are: 1) calibration of the inertial sensing element without external equipment 2) the use of differential inertial sensing equipment to remove the accelerations and rotational rates of a moving vehicle from the pilot’s head-tracking measurements 3) the determination of three-dimensional position and orientation from three corresponding points using a range sensor. The range sensor did not operate as planned. The helmet only managed to remain within the range sensor’s field of view for 37% of flight time. Vertical accelerations showed the greatest correlation when comparing helmet measurements to aircraft measurements. The PHIS operated well during level flight

Improvement of Inertial Navigation System Accuracy Using Alternative Sensors

Katedra měřen

Modified Biaxial Accelerometer Framework in G-sensing Mode

This paper deals with an acceleration measuring unit, which uses two biaxial accelerometers, and compares its performance with a typical triaxial framework. In cases of small aircrafts, UAVs, robots, or terrestrial vehicle navigation units utilizing sensors manufactured by a MEMS technology are preferred due to their cost-effectiveness. In order to suppress imperfections of the measuring system (noise, drift, nonlinearities, small sensitivity) a solution based on the difference configuration of accelerometers is proposed

Quaternionic Attitude Estimation with Inertial Measuring Unit for Robotic and Human Body Motion Tracking using Sequential Monte Carlo Methods with Hyper-Dimensional Spherical Distributions

This dissertation examined the inertial tracking technology for robotics and human tracking applications. This is a multi-discipline research that builds on the embedded system engineering, Bayesian estimation theory, software engineering, directional statistics, and biomedical engineering. A discussion of the orientation tracking representations and fundamentals of attitude estimation are presented briefly to outline the some of the issues in each approach. In addition, a discussion regarding to inertial tracking sensors gives an insight to the basic science and limitations in each of the sensing components. An initial experiment was conducted with existing inertial tracker to study the feasibility of using this technology in human motion tracking. Several areas of improvement were made based on the results and analyses from the experiment. As the performance of the system relies on multiple factors from different disciplines, the only viable solution is to optimize the performance in each area. Hence, a top-down approach was used in developing this system. The implementations of the new generation of hardware system design and firmware structure are presented in this dissertation. The calibration of the system, which is one of the most important factors to minimize the estimation error to the system, is also discussed in details. A practical approach using sequential Monte Carlo method with hyper-dimensional statistical geometry is taken to develop the algorithm for recursive estimation with quaternions. An analysis conducted from a simulation study provides insights to the capability of the new algorithms. An extensive testing and experiments was conducted with robotic manipulator and free hand human motion to demonstrate the improvements with the new generation of inertial tracker and the accuracy and stability of the algorithm. In addition, the tracking unit is used to demonstrate the potential in multiple biomedical applications including kinematics tracking and diagnosis instrumentation. The inertial tracking technologies presented in this dissertation is aimed to use specifically for human motion tracking. The goal is to integrate this technology into the next generation of medical diagnostic system

MEMS Accelerometers

Micro-electro-mechanical system (MEMS) devices are widely used for inertia, pressure, and ultrasound sensing applications. Research on integrated MEMS technology has undergone extensive development driven by the requirements of a compact footprint, low cost, and increased functionality. Accelerometers are among the most widely used sensors implemented in MEMS technology. MEMS accelerometers are showing a growing presence in almost all industries ranging from automotive to medical. A traditional MEMS accelerometer employs a proof mass suspended to springs, which displaces in response to an external acceleration. A single proof mass can be used for one- or multi-axis sensing. A variety of transduction mechanisms have been used to detect the displacement. They include capacitive, piezoelectric, thermal, tunneling, and optical mechanisms. Capacitive accelerometers are widely used due to their DC measurement interface, thermal stability, reliability, and low cost. However, they are sensitive to electromagnetic field interferences and have poor performance for high-end applications (e.g., precise attitude control for the satellite). Over the past three decades, steady progress has been made in the area of optical accelerometers for high-performance and high-sensitivity applications but several challenges are still to be tackled by researchers and engineers to fully realize opto-mechanical accelerometers, such as chip-scale integration, scaling, low bandwidth, etc

Tracking for Mobile 3D Augmented Reality Applications

Ph.DDOCTOR OF PHILOSOPH

Online auto-calibration of triaxial accelerometer with time-variant model structures

© 2017 Elsevier B.V. In this paper, an online auto-calibration method for MicroElectroMechanical Systems (MEMS) triaxial accelerometer (TA) is proposed, which can simultaneously identify the time-dependent model structure and its parameters during the changes of the operating environment. Firstly, the model as well as its associated cost function is linearized by a new proposed linearization approach. Then, exploiting an online sparse recursive least square (SPARLS) estimation, the unknown parameters are identified. In particular, the online sparse recursive method is based on an L1-norm penalized expectation-maximum (EM) algorithm, which can amend the model automatically by penalizing the insignificant parameters to zero. Furthermore, this method can reduce computational complexity and be implemented in a low-cost Micro-Controller-Unit (MCU). Based on the numerical analysis, it can be concluded that the proposed recursive algorithm can calculate the unknown parameters reliably and accurately for most MEMS triaxial accelerometers available in the market. Additionally, this method is experimentally validated by comparing the output estimations before and after calibration under various scenarios, which further confirms its feasibility and effectiveness for online TA calibration

Appl Ergon

The objective of this study was to evaluate the accuracy of various sensor fusion algorithms for measuring upper arm elevation relative to gravity (i.e., angular displacement and velocity summary measures) across different motion speeds. Thirteen participants completed a cyclic, short duration, arm-intensive work task that involved transfering wooden dowels at three work rates (slow, medium, fast). Angular displacement and velocity measurements of upper arm elevation were simultaneously measured using an inertial measurement unit (IMU) and an optical motion capture (OMC) system. Results indicated that IMU-based inclinometer solutions can reduce root-mean-square errors in comparison to accelerometer-based inclination estimates by as much as 87%, depending on the work rate and sensor fusion approach applied. The findings suggest that IMU-based inclinometers can substantially improve inclinometer accuracy in comparison to traditional accelerometer-based inclinometers. Ergonomists may use the non-proprietary sensor fusion algorithms provided here to more accurately estimate upper arm elevation.T42 OH008436/OH/NIOSH CDC HHSUnited States/T42 OH008491/OH/NIOSH CDC HHSUnited States/T42OH008436/ACL/ACL HHSUnited States/T42OH008491/ACL/ACL HHSUnited States/2022-10-26T00:00:00Z29122186PMC960561812055vault:4343

Real-time implementation of some attitude estimation algorithms on a quadrotor UAV / by Siddhant Nayak.

The recent developments in research pertaining to the field of Unmanned Aerial Vehicles (UAVs) is motivated by its technical challenges as well as its practical implications in areas where human presence is inefficient, redundant or dangerous. The absence of human interference requires more robust and precise control techniques. However, most modern attitude control techniques require the knowledge of the current orientation of the body. There is no sensor available that explicitly measures the attitude of a rigid body and hence, for small scale UAVs. it must be estimated using inertial vector measurements from low-cost and low-weight Micro-Electro-Mechanical System (MEMS) sensors like gyroscopes, accelerometers and magnetometers. The predominant attitude representation formulations of a rigid body in three-dimensional space are recapitulated to elucidate the dynamical model of a quadrotor UAV. Low-cost MEMS are prone to significant noise effects from temperature change, vibrations, on-board magnetic fields generated by motors and currents. To improve the accuracy of the measurements sensor calibration techniques are explored. Primitive attitude estimation techniques like TRIAD, Davenports q-method, QUEST.FOAM, SVD method, etc. (which were aimed to be static optimization solutions to Wahbas Problem) were reviewed. These algorithms were extended to incorporate filtering techniques like Kahnan-type, to handle the measurement noise, and complementary filtering, where sensor measurements are fused to reconstruct the orientation of a rigid body. Tlie latest nonlinear observers are also discussed for implementation purposes. Practical implementation and performance comparison of various attitude estimation algorithms has been conducted on a small-scale quadrotor UAV, consisting of an inertial measurement unit (3-axis gyroscope, accelerometer and magnetometer), microcontroller, brushless motors, electronic speed controllers, on-board power supply and necessary frame constructs

Development of a Novel Handheld Device for Active Compensation of Physiological Tremor

In microsurgery, the human hand imposes certain limitations in accurately positioning the tip of a device such as scalpel. Any errors in the motion of the hand make microsurgical procedures difficult and involuntary motions such as hand tremors can make some procedures significantly difficult to perform. This is particularly true in the case of vitreoretinal microsurgery. The most familiar source of involuntary motion is physiological tremor. Real-time compensation of tremor is, therefore, necessary to assist surgeons to precisely position and manipulate the tool-tip to accurately perform a microsurgery. In this thesis, a novel handheld device (AID) is described for compensation of physiological tremor in the hand. MEMS-based accelerometers and gyroscopes have been used for sensing the motion of the hand in six degrees of freedom (DOF). An augmented state complementary Kalman filter is used to calculate 2 DOF orientation. An adaptive filtering algorithm, band-limited Multiple Fourier linear combiner (BMFLC), is used to calculate the tremor component in the hand in real-time. Ionic Polymer Metallic Composites (IPMCs) have been used as actuators for deflecting the tool-tip to compensate for the tremor