Design and Configuration of Folded Platonic Strapdowns of Biaxial MEMS Accelerometers

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Xiaowei Shan ◽  
Jorge Angeles ◽  
James Richard Forbes
Keyword(s):  
Embedded System ◽  
Position Estimation ◽  
Measurement Unit ◽  
Signal Acquisition ◽  
Platonic Solids ◽  
Electromechanical Properties ◽  
Mems Accelerometers ◽  
Estimation Precision ◽  
Mems Imu ◽  
Pose And Twist

Abstract The authors report on the design, configure, and test of isotropic accelerometer strapdowns for high-precision inertial measurement unit (IMU) and folded MEMS configuration. The biaxial low-g MEMS accelerometers are based on the Platonic solids. A Platonic strapdown is integrated into an embedded system for acceleration-array signal acquisition targeting rigid-body pose-and-twist estimation. The electromechanical properties for dynamic sensitivity are tested on a haptic manipulator, which shows that the position estimation matches reasonably well the encoder readouts. The Platonic strapdown is promising in folded MEMS IMU with chip-level miniaturization and high estimation precision.

scholarly journals Singular Value Decomposition in Embedded Systems Based on ARM Cortex-M Architecture

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 34
Author(s):  
Michele Alessandrini ◽  
Giorgio Biagetti ◽  
Paolo Crippa ◽  
Laura Falaschetti ◽  
Lorenzo Manoni ◽  
...  
Keyword(s):  
Embedded Systems ◽  
Singular Value Decomposition ◽  
Embedded System ◽  
Mimo Systems ◽  
Singular Value ◽  
Measurement Unit ◽  
Comprehensive Treatment ◽  
Mathematical Tool ◽  
Value Decomposition ◽  
Speed Accuracy

Singular value decomposition (SVD) is a central mathematical tool for several emerging applications in embedded systems, such as multiple-input multiple-output (MIMO) systems, data analytics, sparse representation of signals. Since SVD algorithms reduce to solve an eigenvalue problem, that is computationally expensive, both specific hardware solutions and parallel implementations have been proposed to overcome this bottleneck. However, as those solutions require additional hardware resources that are not in general available in embedded systems, optimized algorithms are demanded in this context. The aim of this paper is to present an efficient implementation of the SVD algorithm on ARM Cortex-M. To this end, we proceed to (i) present a comprehensive treatment of the most common algorithms for SVD, providing a fairly complete and deep overview of these algorithms, with a common notation, (ii) implement them on an ARM Cortex-M4F microcontroller, in order to develop a library suitable for embedded systems without an operating system, (iii) find, through a comparative study of the proposed SVD algorithms, the best implementation suitable for a low-resource bare-metal embedded system, (iv) show a practical application to Kalman filtering of an inertial measurement unit (IMU), as an example of how SVD can improve the accuracy of existing algorithms and of its usefulness on a such low-resources system. All these contributions can be used as guidelines for embedded system designers. Regarding the second point, the chosen algorithms have been implemented on ARM Cortex-M4F microcontrollers with very limited hardware resources with respect to more advanced CPUs. Several experiments have been conducted to select which algorithms guarantee the best performance in terms of speed, accuracy and energy consumption.

scholarly journals MEMS Based SINS/OD Filter for Land Vehicles’ Applications

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Huisheng Liu ◽  
Zengcai Wang ◽  
Susu Fang ◽  
Chao Li
Keyword(s):  
Navigation System ◽  
Velocity Measurement ◽  
Microelectromechanical Systems ◽  
Low Cost ◽  
Detection Algorithm ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Land Vehicles ◽  
Mems Imu

A constrained low-cost SINS/OD filter aided with magnetometer is proposed in this paper. The filter is designed to provide a land vehicle navigation solution by fusing the measurements of the microelectromechanical systems based inertial measurement unit (MEMS IMU), the magnetometer (MAG), and the velocity measurement from odometer (OD). First, accelerometer and magnetometer integrated algorithm is studied to stabilize the attitude angle. Next, a SINS/OD/MAG integrated navigation system is designed and simulated, using an adaptive Kalman filter (AKF). It is shown that the accuracy of the integrated navigation system will be implemented to some extent. The field-test shows that the azimuth misalignment angle will diminish to less than 1°. Finally, an outliers detection algorithm is studied to estimate the velocity measurement bias of the odometer. The experimental results show the enhancement in restraining observation outliers that improves the precision of the integrated navigation system.

Comparison of UGV Position Estimation Equipped With GNSS-RTK and GPS Using EKF

2020 ◽  
Author(s):  
Hesham Ismail ◽  
Thani Althani ◽  
Mohammed Minhas Anzil ◽  
Prashanth Subramaniam
Keyword(s):  
Extreme Temperature ◽  
Processing Technique ◽  
Position Estimation ◽  
Image Processing Technique ◽  
Measurement Unit ◽  
Unmanned Ground Vehicle ◽  
Camera System ◽  
Ground Vehicle ◽  
Positioning Device ◽  
Global Navigation Satellite

Abstract Site assessments for bifacial Photovoltaic (PV) installation are quite challenging to conduct manually due to the area size and the extreme temperature conditions at desert sites. We designed and built an autonomous Unmanned Ground Vehicle (UGV) fitted with a Global Navigation Satellite Network-System Real-Time Kinematic (GNSS-RTK) positioning device, an Inertial Measurement Unit (IMU), encoder to improve and aid site assessments in desert condition. Sandy terrains deserts are challenging for UGV’s because they increase the likelihood of wheel slippage due to reduced traction. Sensor details such as IMU, GNSS-RTK, and encoder should be taken into consideration to account for the errors that the desert terrains pose. This study compared the Extended Kalman Filter (EKF) for standard GPS & GNSS-RTK to verify which performs better for the UGV’s position estimation. The estimated UGV’s position from the kinematics model and EKF are validated using a drone camera system that uses an image processing technique to verify the UGV’s position with the help of the visible reference cones. Throughout the experiments, the GNSS-RTK performed better than GPS. Also, the EKF performed as well as the GNSS-RTK by trusting it more than the encoder/gyroscope reading.

scholarly journals Performance Enhancement of Pedestrian Navigation Systems Based on Low-Cost Foot-Mounted MEMS-IMU/Ultrasonic Sensor

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 364 ◽  
Author(s):  
Ming Xia ◽  
Chundi Xiu ◽  
Dongkai Yang ◽  
Li Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Low Cost ◽  
Ultrasonic Sensor ◽  
Measurement Unit ◽  
Positioning Error ◽  
Process Noise ◽  
Pedestrian Navigation ◽  
Mems Imu

The pedestrian navigation system (PNS) based on inertial navigation system-extended Kalman filter-zero velocity update (INS-EKF-ZUPT or IEZ) is widely used in complex environments without external infrastructure owing to its characteristics of autonomy and continuity. IEZ, however, suffers from performance degradation caused by the dynamic change of process noise statistics and heading estimation errors. The main goal of this study is to effectively improve the accuracy and robustness of pedestrian localization based on the integration of the low-cost foot-mounted microelectromechanical system inertial measurement unit (MEMS-IMU) and ultrasonic sensor. The proposed solution has two main components: (1) the fuzzy inference system (FIS) is exploited to generate the adaptive factor for extended Kalman filter (EKF) after addressing the mismatch between statistical sample covariance of innovation and the theoretical one, and the fuzzy adaptive EKF (FAEKF) based on the MEMS-IMU/ultrasonic sensor for pedestrians was proposed. Accordingly, the adaptive factor is applied to correct process noise covariance that accurately reflects previous state estimations. (2) A straight motion heading update (SMHU) algorithm is developed to detect whether a straight walk happens and to revise errors in heading if the ultrasonic sensor detects the distance between the foot and reflection point of the wall. The experimental results show that horizontal positioning error is less than 2% of the total travelled distance (TTD) in different environments, which is the same order of positioning error compared with other works using high-end MEMS-IMU. It is concluded that the proposed approach can achieve high performance for PNS in terms of accuracy and robustness.

scholarly journals Comparative Analysis between Error-State and Full-State Error Estimation for KF-Based IMU/GNSS Integration against IMU Faults

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4912 ◽  
Author(s):  
Wei Liu ◽  
Dan Song ◽  
Zhipeng Wang ◽  
Kun Fang
Keyword(s):  
Satellite System ◽  
Position Estimation ◽  
Measurement Unit ◽  
Multisensor Systems ◽  
System Integrity ◽  
Full State ◽  
Tightly Coupled ◽  
Integrity Assurance ◽  
Error State ◽  
State Error

Considering the inertial measurement unit (IMU) faults risk of an unmanned aerial vehicle (UAV), this paper provides an analysis of the error overboundings of position estimation in a tightly coupled IMU/global navigation satellite system (GNSS) integrated architecture under the IMU fault conditions using an error-state EKF-based approach and provides a comparison to a recently published EKF-based full state method. Simulation results show that both the error overboundings of the error-state and full-state EKFs can fit the state error against the IMU faults, but the error-state EKF is more suitable for UAV navigation system integrity assurance due to its higher calculation effciency. This study will be extended to the integrity monitoring of multisensor systems.

scholarly journals An Adaptive UWB/MEMS-IMU Complementary Kalman Filter for Indoor Location in NLOS Environment

2019 ◽  
Vol 11 (22) ◽  
pp. 2628 ◽  
Author(s):  
Liu ◽  
Li ◽  
Wang ◽  
Zhang
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Inertial Measurement Unit ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Indoor Location ◽  
Magnetometer Data ◽  
Noise Covariance ◽  
Mems Imu ◽  
Non Line Of Sight

High precision positioning of UWB (ultra-wideband) in NLOS (non-line-of-sight) environment is one of the hot issues in the direction of indoor positioning. In this paper, a method of using a complementary Kalman filter (CKF) to fuse and filter UWB and IMU (inertial measurement unit) data and track the errors of variables such as position, speed, and direction is presented. Based on the uncertainty of magnetometer and acceleration, the noise covariance matrix of magnetometer and accelerometer is calculated dynamically, and then the weight of magnetometer data is set adaptively to correct the directional error of gyroscope. Based on the uncertainty of UWB distance observations, the covariance matrix of UWB measurement noise is calculated dynamically, and then the weight of UWB data observations is set adaptively to correct the position error. The position, velocity and direction errors are corrected by the fusion of UWB and IMU. The experimental results show that the algorithm can reduce the gyroscope deviation with magnetic noise and motion noise, so that the orientation estimates can be improved, as well as the positioning accuracy can be increased with UWB ranging noise.

scholarly journals Real-Time Onboard 3D State Estimation of an Unmanned Aerial Vehicle in Multi-Environments Using Multi-Sensor Data Fusion

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 919 ◽  
Author(s):  
Hao Du ◽  
Wei Wang ◽  
Chaowen Xu ◽  
Ran Xiao ◽  
Changyin Sun
Keyword(s):  
Data Fusion ◽  
State Estimation ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Position Estimation ◽  
Sensor Data ◽  
Measurement Unit ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Aerial Vehicle

The question of how to estimate the state of an unmanned aerial vehicle (UAV) in real time in multi-environments remains a challenge. Although the global navigation satellite system (GNSS) has been widely applied, drones cannot perform position estimation when a GNSS signal is not available or the GNSS is disturbed. In this paper, the problem of state estimation in multi-environments is solved by employing an Extended Kalman Filter (EKF) algorithm to fuse the data from multiple heterogeneous sensors (MHS), including an inertial measurement unit (IMU), a magnetometer, a barometer, a GNSS receiver, an optical flow sensor (OFS), Light Detection and Ranging (LiDAR), and an RGB-D camera. Finally, the robustness and effectiveness of the multi-sensor data fusion system based on the EKF algorithm are verified by field flights in unstructured, indoor, outdoor, and indoor and outdoor transition scenarios.

scholarly journals Multi-Sensor Orientation Tracking for a Façade-Cleaning Robot

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1483 ◽  
Author(s):  
Manuel Vega-Heredia ◽  
Ilyas Muhammad ◽  
Sriharsha Ghanta ◽  
Vengadesh Ayyalusami ◽  
Siti Aisyah ◽  
...  
Keyword(s):  
Orientation Angle ◽  
Position Estimation ◽  
Measurement Unit ◽  
Vision Sensor ◽  
Window Frame ◽  
Cleaning Robot ◽  
Heading Angle ◽  
Glass Facade ◽  
Cleaning Robots ◽  
Glass Façade

Glass-façade-cleaning robots are an emerging class of service robots. This kind of cleaning robot is designed to operate on vertical surfaces, for which tracking the position and orientation becomes more challenging. In this article, we have presented a glass-façade-cleaning robot, Mantis v2, who can shift from one window panel to another like any other in the market. Due to the complexity of the panel shifting, we proposed and evaluated different methods for estimating its orientation using different kinds of sensors working together on the Robot Operating System (ROS). For this application, we used an onboard Inertial Measurement Unit (IMU), wheel encoders, a beacon-based system, Time-of-Flight (ToF) range sensors, and an external vision sensor (camera) for angular position estimation of the Mantis v2 robot. The external camera is used to monitor the robot’s operation and to track the coordinates of two colored markers attached along the longitudinal axis of the robot to estimate its orientation angle. ToF lidar sensors are attached on both sides of the robot to detect the window frame. ToF sensors are used for calculating the distance to the window frame; differences between beam readings are used to calculate the orientation angle of the robot. Differential drive wheel encoder data are used to estimate the robot’s heading angle on a 2D façade surface. An integrated heading angle estimation is also provided by using simple fusion techniques, i.e., a complementary filter (CF) and 1D Kalman filter (KF) utilizing the IMU sensor’s raw data. The heading angle information provided by different sensory systems is then evaluated in static and dynamic tests against an off-the-shelf attitude and heading reference system (AHRS). It is observed that ToF sensors work effectively from 0 to 30 degrees, beacons have a delay up to five seconds, and the odometry error increases according to the navigation distance due to slippage and/or sliding on the glass. Among all tested orientation sensors and methods, the vision sensor scheme proved to be better, with an orientation angle error of less than 0.8 degrees for this application. The experimental results demonstrate the efficacy of our proposed techniques in this orientation tracking, which has never applied in this specific application of cleaning robots.

scholarly journals A Low-Cost Chamber Prototype for Automatic Thermal Analysis of MEMS IMU Sensors in Tilt Measurements Perspective

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2705
Author(s):  
Giuseppe Ruzza ◽  
Luigi Guerriero ◽  
Paola Revellino ◽  
Francesco M. Guadagno
Keyword(s):  
Thermal Analysis ◽  
Thermal Behavior ◽  
Low Cost ◽  
Measurement Unit ◽  
Peltier Element ◽  
Micro Electro Mechanical Systems ◽  
Thermal Cell ◽  
3D Printed ◽  
Mems Imu ◽  
System Prototype

In this work, a low-cost, open-source and replicable system prototype for thermal analysis of low-cost Micro Electro-Mechanical Systems (MEMS) Inertial Measurement Unit (IMU) sensors in tilt measurement perspective is presented and tested. The system is formed of a 3D printed frame, a thermal cell consisting in a Peltier element mounted over a heat sink, and a control and power system. The frame is designed to allow the independent biaxial tilting of the thermal cell through two servomotors. The control board is formed by an Arduino® and a self-made board including a power drive for controlling the thermal unit and servomotors. We tested the chamber analyzing the behavior of multiple MEMS IMU onboard accelerometers suitable for measuring tilt. Our results underline the variability of the thermal behavior of the sensors, also for different sensor boards of the same model, and consequently the need for the adoption of a thermal compensation strategy based on thermal analysis results. These data suggesting the need for the analysis of the thermal behavior of MEMS-based sensors, indicate the potential of our system in making low-cost sensors suitable in medium-to-high precision monitoring applications.

scholarly journals Implementation of the centralized control system for drone training

2018 ◽  
Vol 7 (3.3) ◽  
pp. 379
Author(s):  
Bong Hyun Kim
Keyword(s):  
Control System ◽  
Angular Velocity ◽  
Embedded System ◽  
Flight Control ◽  
Attitude Control ◽  
Test System ◽  
Measurement Unit ◽  
Centralized Control ◽  
Structural Problems ◽  
Earth Magnetic Field

Background/Objectives: The drones became a representative item in the IoT era. However, there is no drone pilot test system that can safely train this in the education field. Drones have very dangerous structural problems, so it is very necessary to practice them easily. Therefore, it is necessary to develop a system that can control the drones safely and easily while controlling them.Methods/Statistical analysis: In this paper, we will develop software for controlling a dedicated board platform that can securely perform ground testing by mounting four drones of motor and drive on a board (PCB). To this end, we supported various control IMU (Inertial Measurement Unit) boards for attitude control by using sensor which is the core technology of drone flight control. Also, Acceleration Data, Angular Velocity Data, Earth Magnetic Field Data, and Atmospheric Pressure Data for maintaining the altitude were used for the drone flight.Findings: In the implemented central control system, the AT chip is built in and designed to perform all control related to the flight of the drone. In addition, since it is an embedded system, we have programmed the attitude control using the sensor, the motor output setting, and the controller connection information. The CPU required for drones control can be replaced with various types of controllers besides Fno Arduino, UNO, Muiltiwii. For this purpose, the main PCB is designed so that the power supply terminal can be used for each CPU. Finally, it was developed as a setup program to correct the sensor and output of the drone.Improvements/Applications: The system implemented in this paper can easily control the drone. In addition, acceleration, angular velocity, geomagnetic field, air pressure sensor, GPS, etc. necessary for drone control can be utilized by stabilizing the initial set value. In other words, the zero point of the sensor can be captured and the signal appropriate to the current state of the drone can be stored in the processor.  

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