Attitude Measurement Unit Research Articles

Optimization of Geomagnetic and Solar Reference Vector Algorithms for Micro-Nano Satellites

To optimize the geomagnetic reference vector and the solar reference vector calculation method, this paper proposes a geomagnetic fitting algorithm and simplifies the DE430 ephemeris structure to speed up the determination rate of the geomagnetic and solar reference vectors. The two optimized reference vector determination methods are simulated on the Attitude Measurement Unit (AMU). The simulation results show that the single-step elapsed time of the geomagnetic fitting algorithm is reduced by 99.5% compared with the IGRF-13 geomagnetic model algorithm, and the data capacity is reduced by 97.3% compared with the look-up table method. The data capacity of the simplified ephemeris algorithm is 66.9% lower than that of the DE430 ephemeris algorithm, and the calculation accuracy is nearly 3 orders of magnitude higher than that of the approximate calculation method. This method can significantly shorten the attitude determination time and increase the attitude determination frequency, dramatically improving attitude determination accuracy and attitude control accuracy. The AMU supported by these two algorithms has sound engineering practical value.

Computational Technologies for the Wearable Sports Training System Based on Sensor Acquisition

Aiming at the problems of long time-consuming and low precision in traditional training system, a wearable sports training system based on data mining is proposed and designed. According to the overall structure of the system, the hardware and software of the system are designed respectively. The hardware part is mainly composed of attitude measurement unit, power supply unit, main control unit, clock circuit, and reset circuit. The software part introduces the data mining method of mining sports training information data. The experimental results show that the system designed in this study effectively shortens the time-consuming time, improves the accuracy, and has certain advantages.

Implementation of Snow and Ice Sports Health and Sports Information Collection System Based on Internet of Things.

The promotion of ice and snow sports not only provides professional athletes for the Winter Olympics but also acts as appreciative mass bases for ice and snow sports. The appearance of ice and snow sports will bring a new consumption pattern and develop a new ice and snow industry. In this paper, an Internet of Things (IoT)-based sports information collection system which is specifically designed and developed for the healthcare domain specifically in the snow and ice sports is proposed. The physiological parameters such as body temperature, ECG, blood pressure, blood sugar, and blood oxygen saturation are captured through various monitoring devices. These physiological parameters are transmitted to the mobile device by the wireless module and mobile device that receives and displays these physiological parameters. A complete hardware design of the whole ice and snow sports health and sports information acquisition system, which is based on the Internet of Things, is given, and then, there is the overall design scheme of the system, such as adopted modular design for the system, attitude measurement unit, UWB positioning unit, data storage, and communication unit, respectively. The measurement results of the professional medical equipment are compared with those of acquisition equipment in real environment of ice and sports. These results have verified accuracy of data collected by acquisition equipment and meet the design requirements of the proposed system.

Sports training monitoring of energy-saving IoT wearable devices based on energy harvesting

In this paper, energy-saving the Internet of Things wearable devices based on energy harvesting used to monitor sports training. The energy sources involved include radiofrequency energy, mechanical energy, and thermal energy. The power management circuit structure designed to match its application with energy harvesting components as the core to ensure that the collected energy can meet the power conditions of electrical equipment. Completed the hardware design of the entire human motion information collection system based on energy-saving IoT wearable devices, first gave the overall design plan of the system, and on this basis, adopted the modular design of the system, and designed the posture measurement separately unit, positioning unit, and data storage and communication unit. Among them, the attitude measurement unit designed according to the different needs in actual use, and the miniature attitude measurement unit and the multi-function attitude measurement unit are designed. The data storage and communication unit designed to ensure data storage and for the reliability of transmission, an offline data storage unit and a Bluetooth remote communication unit designed respectively. The system adopts a modular design, which makes the system more convenient to expand and upgrade, and has better applicability and reliability.

Calibration of Low Cost IMU’s Inertial Sensors for Improved Attitude Estimation

To better solve the attitude of robots with high precision using low cost inertial measurement unit (IMU), we design novel calibration methods, namely twelve positions calibration method, method based on ellipsoid fitting and eight positions calibration method, to effectively calibrate accelerometer, magnetometer and gyroscope, respectively. An attitude estimator using data from the multi-sensors calibrated by our proposed methods was designed based on Kalman Filter, with its estimated value of the attitude prediction deviation fed back to the predicted value as the prediction deviation at the end of each filtering period. Finally, relevant experiments are designed to verify the validity of the proposed attitude solution method as well as the proposed calibration methods, with results showing that the roll and pitch angle measured by the attitude measurement unit have an effective resolution of 0.1° and the yaw angle has an effective resolution of 1°. The innovation of this paper is that the proposed calibration methods can be carried out with simple tools, eliminating the need for expensive and complicated multi-axis turntables, and the designed attitude measurement unit based on calibrated low cost IMU’s inertial sensors has smaller size, higher resolution in roll and pitch, and much lower cost compared with the commercial MTi-G-710. The accuracy of the attitude solution for such IMU is high enough for the application on robots.

High-Accuracy and Low-Cost Attitude Measurement Unit of the CubeSat

This paper proposes high-accuracy and reliable attitude measurement methods exclusive for CubeSat with restrictions of low cost, limited space, and low power consumption. The attitude measurement unit is equipped with Commercial Off-The-Shelf (COTS) components including Micro-Electro-Mechanical System (MEMS) gyro and two simultaneously operating star trackers (STR) to enhance the measurement accuracy. The Multiplicative Extended Kalman Filter (MEKF) is used to estimate the attitude of CubeSat, and four kinds of attitude estimation layouts are put forward according to the idea of weighted average of two quaternions from two STR and different architectures of information fusion. Using the proposed methods, the attitude measurement unit can continuously provide accurate and reliable attitude knowledge for attitude control unit when the CubeSat is running in orbit. Numerical simulation is performed to verify the effectiveness of the proposed methods, and it offers a reference for CubeSat developers from the perspective of engineering application.

A Novel Joint Angle Estimation Method for Serial Manipulator Using Micro-Electromechanical Systems Sensors

This article focuses on the use of low-cost micro-electromechanical systems (MEMS) magnetic, angular rate, and gravity (MARG) sensors in the field of estimating the joint angles of revolute serial manipulators. In particular, an easy-to-install joint angle estimation method, named the incremental decomposition method (IDM), is proposed. It allows the MEMS MARG sensors to be mounted in any attitude on any position of the manipulator's links, with at least one attitude measurement unit on the output side of each joint. The IDM is completely independent from the encoder-based joint angle estimator. Hence, it can work as an assistant fault-detection criterion for the economical robotic arms. A robotic arm with six degrees of freedom is used as the experimental setup. The IDM can estimate the manipulator's joint angles by fusing the attitude quaternion output by the MARG sensors. To validate the performances and limitations of this method, five experiments have been accomplished in this article. The experimental results are reported in order, in comparison with the high-resolution encoders.

Design and Implementation of a Novel Tilt Sensor Based on the Principle of Variable Reluctance.

Tilt angle measurement in dynamic systems is problematic because the rotation of the measured platform is coupled with translation. Therefore, when some sensors are applied in dynamic systems, their output signals are often submerged in the noise signals generated by translation. To enhance the ability of tilt sensors to resist translational noise, a dynamic tilt sensor is proposed based on the principle of variable reluctance from the perspective of sensor structure. The eccentric structure of the sensor constructed with a shell, liquid, and internal damping plate was designed according to the principles of mechanics. The characteristic of translational acceleration restraint determined by the sensor structure was established theoretically. In addition, the magnetic circuit of the sensor was analyzed to illustrate the sensor’s working principles. A Clapp oscillator circuit was designed to convert mechanical motion into a measureable electrical signal. A method to determine the sensor’s direction of rotation is proposed. A waveform conversion circuit was designed to convert the sine wave output of the Clapp oscillator to a square wave, and a square-wave frequency measurement circuit was designed based on the C8051 micro-control unit. A translation–rotation experimental hardware platform was constructed. The data acquisition program was designed on a PC platform, and the translation–rotation experiments were conducted with an MTi attitude measurement unit as a reference. The validity of the tilt angle measurements and the effect of the translational acceleration restraint of the sensor were verified by the experimental data. The theoretical results obtained were consistent with the experimental data, verifying the validity of the theoretical analysis and experimental devices employed. A measurement range of −180 to 180° was achieved.

Development of a Small-sized and Low-cost Attitude Measurement Unit for Agricultural Robot Application

The objective of this study was to develop a small-sized and low-cost unit to provide attitude measurements for lightloaded, small-sized and cost effective agricultural robot application. The attitude measurement unit comprised an electronic control unit (ECU) and a gyroscope and an accelerometer within a small-sized and low-cost IMU. In order to avoid the measurement limitations of a single sensor, a self-adaptive complementary filter and a Kalman filter were discussed and compared for sensor fusion. By comparison, in respect of preventing angle drift and maintaining dynamic characteristics, the Kalman filter has the significant advantage, especially in dynamic motion. In the comparison with a highly precise aviation-level fiber optic gyroscope (FOG), the results showed that the static angle drift was restrained by Kalman filter which reached the performance of the FOG. And in the series of farm experiments, the dynamic characteristic of the developed attitude measurement unit is close to the FOG performance in the sub-degree level. This is an acceptable accuracy for light-loaded, small-sized and cost effective agricultural robot application such as agriculture drone, greenhouse robots, harvesting robot arm and so on.

Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors.

Gait phase is widely used for gait trajectory generation, gait control and gait evaluation on lower-limb exoskeletons. So far, a variety of methods have been developed to identify the gait phase for lower-limb exoskeletons. Angular sensors on lower-limb exoskeletons are essential for joint closed-loop controlling; however, other types of sensors, such as plantar pressure, attitude or inertial measurement unit, are not indispensable.Therefore, to make full use of existing sensors, we propose a novel gait phase recognition method for lower-limb exoskeletons using only joint angular sensors. The method consists of two procedures. Firstly, the gait deviation distances during walking are calculated and classified by Fisher’s linear discriminant method, and one gait cycle is divided into eight gait phases. The validity of the classification results is also verified based on large gait samples. Secondly, we build a gait phase recognition model based on multilayer perceptron and train it with the phase-labeled gait data. The experimental result of cross-validation shows that the model has a 94.45% average correct rate of set (CRS) and an 87.22% average correct rate of phase (CRP) on the testing set, and it can predict the gait phase accurately. The novel method avoids installing additional sensors on the exoskeleton or human body and simplifies the sensory system of the lower-limb exoskeleton.

Inertial/celestial-based fuzzy adaptive unscented Kalman filter with Covariance Intersection algorithm for satellite attitude determination

This paper deals with the attitude determination algorithm for the satellite with an attitude measurement unit that is comprised of one gyroscope and two star trackers installed perpendicularly. Since the data updating rate of star trackers is typically lower than that of the gyro and filter, appropriate compensation is made for star sensors, but this results in more difficulties of determining the performed noise level. A fuzzy adaptive tuning method is used to help tuning, and with modified Rodrigues parameters and rotation vector to represent attitude error, a fuzzy adaptive unscented Kalman filter with minimal skew sampling method is realized, which works as a sub-system and estimates sub-optimal attitude states and gyro bias. Two such sub-systems are federated into the framework of Covariance Intersection algorithm to achieve data fusion for an optimal attitude and gyro bias estimation in system level. Simulation is performed to verify the attitude determination algorithm presented in this paper.

Inertial Navigation Aiding by Stationary Updates

Sensor-aided inertial navigation has successfully been used for decades for localization of a roving body. When the rover is known to be stationary, artificial “stationary” measurements (i.e., zero velocity and/or zero angular rate) may be imposed. This corrects the velocity, attitude, and inertial measurement unit (IMU) biases, which decreases the rate of drift of the position and attitude. Implementation requires reliable automated tests to detect periods when the vehicle is stationary. Due to cost concerns, methods that use sensors that are already on the vehicle are preferred. This paper reviews existing stationary detection methods and proposes a new frequency domain approach, using only IMU data, to detect stationarity, with specifications and analysis for land vehicles. The performance of this new approach is evaluated in both theory and practice. In addition, this paper presents analytic and numeric evaluations of the observability of the inertial navigation system (INS) error states with stationary updates. Improvements in localization performance in an INS with stationary detection and aiding is shown experimentally.

Design and Realization of Modular MEMS-Based Attitude Measurement and Control System Based Wireless Sensor Networks

The Modular Platform System in this Paper Consists of a Series of Modularized MEMS Subsystems, Including Central Control Unit, MEMS Attitude Measurement Unit, Environment Unit, Flight Measurement and Control Unit, Time Control Unit, Impact Vibration Measurement Unit and other Auxiliary Modular Units. the Key of the System Is the MEMS Attitude Measurement Unit, which Can Resolve the Real Time Attitude Information of the Carrier. the System Platform Chooses Different Sensors and Units According to Various Circumstances and Implements Data Collection, Fusion and Solution through the Central Processing Module. by Means of Carrying Distinct Units, System Platform Can Constitute many Measurement Control Systems, such as Micro Wireless Attitude Determination System, MAV Flight Data Recorder, MAV Flight Control System and PHM (prognostic and Health Management). with Application of MEMS, the System in this Paper Is High-Efficient, Low-Weighted, Simple-Structured, High-Reliable and even Can Respond in a Short Time. in View of these Merits, the System Can Be Applied in many Kinds of Attitude Measurement and Control System.

Design and Test of MEMS Attitude Measurement Unit for Fall Detection

Fall is a risky event in the elderly people’s daily life, it often cause serious injury both in physiology and psychology. A MEMS attitude measurement system is designed for fall detection in real time. This paper presents the design and error test of the attitude measurement unit. Each unit contains orthogonally mounted triads of accelerometers, magnetometers and gyros. With an integrated microcontroller for attitude calculating and flash for data storage, the size of the unit is 32mm×23mm×12mm. An extended Kalman filter based on quaternions is designed for attitude measurement. The digital angle output rate is 100Hz. A new method based on coordinate transformation for attitude measurement error test is introduced, using a single axis turntable and a fixed angle wedge. Theory of the testing method is presented and test experiments are performed. Test results show that attitude measurement error is less than 2°, which meets the requirement of fall detection precision. The fall detection system consists of five attitude measurement units fixed on the human legs and waist.

Design of a three-dimensional near real-time image processing system

The airborne three-dimensional (3D) imager, which integrates global positioning system (GPS), attitude measurement unit (AMU), scanning laser rangefinder (SLR) and spectral scanner, has been developed successfully. The spectral scanner and SLR use the same optical system, which ensures that the laser point matches pixel seamlessly. The distinctive advantage of the airborne 3D imager is that it can produce geo-referenced imagery and DSM (digital surface models) without any ground control points (GCPs). It is no longer necessary to obtain GCPs, and the software can process the data to produce DSM and a geo-referenced image in near real-time, so the airborne 3D imager is many times faster than the traditional approaches. The processing procedure of the software involves decomposing and checking the raw data, processing GPS data, calculating the positions of laser sample points, producing a geo-referenced image, producing DSM, and mosaicking strips. In this paper, we introduce the principle of the airborne 3D imager first and then focus on the fast processing algorithm. The flight tests and processed results demonstrate that the processing technique is feasible, and can meet the requirement of near real-time applications.

Data processing technology of airborne 3D image

Airborne 3D image which integrates GPS, attitude measurement unit (AMU), scanning laser rangefinder (SLR) and spectra scanner has been developed successfully. The spectral scanner and SLR use the same optical system which ensures laser point to match pixel seamlessly. The distinctive advantage of 3D image is that it can produce geo-referenced images and DSM (digital surface models) images without any ground control points (GCPs). It is no longer necessary to survey GCPs and with some softwares the data can be processed and produce digital surface models (DSM) and geo-referenced images in quasi-real-time, therefore the efficiency of 3D image is 10–100 times higher than that of traditional approaches. The processing procedure involves decomposing and checking the raw data, processing GPS data, calculating the positions of laser sample points, producing geo-referenced image, producing DSM and mosaicing strips.