Low cost automation using INS/GPS data fusion for accurate positioning

Robotica ◽  
2003 ◽  
Vol 21 (3) ◽  
pp. 255-260 ◽  
Author(s):  
J. Z. Sasiadek ◽  
Q. Wang
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Autonomous Vehicles ◽  
Low Cost ◽  
Real Time Control ◽  
Adaptive Kalman Filter ◽  
Time Control ◽  
Global Positioning ◽  
And Control ◽  
Fuzzy Adaptive

Low cost automation often requires accurate positioning. This happens whenever a vehicle or robotic manipulator is used to move materials, parts or minerals on the factory floor or outdoors. In last few years, such vehicles and devices are mostly autonomous. This paper presents the method of sensor fusion based on the Adaptive Fuzzy Kalman Filtering. This method has been applied to fuse position signals from the Global Positioning System (GPS) and Inertial Navigation System (INS) for the autonomous mobile vehicles. The presented method has been validated in 3-D environment and is of particular importance for guidance, navigation, and control of mobile, autonomous vehicles. The Extended Kalman Filter (EKF) and the noise characteristic have been modified using the Fuzzy Logic Adaptive System and compared with the performance of regular EKF. It has been demonstrated that the Fuzzy Adaptive Kalman Filter gives better results (more accurate) than the EKF. The presented method is suitable for real-time control and is relatively inexpensive. Also, it applies to fusion process with sensors different than INS or GPS.

scholarly journals Navigation System Heading and Position Accuracy Improvement through GPS and INS Data Fusion

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Ji Hyoung Ryu ◽  
Ganduulga Gankhuyag ◽  
Kil To Chong
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Navigation System ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Navigation Systems ◽  
Position Information ◽  
Global Positioning ◽  
Heading Angle ◽  
Electronic Compass

Commercial navigation systems currently in use have reduced position and heading error but are usually quite expensive. It is proposed that extended Kalman filter (EKF) and Unscented Kalman Filter (UKF) be used in the integration of a global positioning system (GPS) with an inertial navigation system (INS). GPS and INS individually exhibit large errors but they do complement each other by maximizing the advantage of each in calculating the heading angle and position through EKF and UKF. The proposed method was tested using low cost GPS, a cheap electronic compass (EC), and an inertial management unit (IMU) which provided accurate heading and position information, verifying the efficacy of the proposed algorithm.

Design of a Smart Green Energy Management System Based on DMX512 Protocol

2013 ◽  
Vol 479-480 ◽  
pp. 1032-1037
Author(s):  
Shinn Fwu Wang ◽  
Yi Zhan Su ◽  
Yi Chu ◽  
Yuan Fong Chau ◽  
Jeng Hua Wei ◽  
...  
Keyword(s):  
Energy Management ◽  
Management System ◽  
Low Cost ◽  
Green Energy ◽  
Energy Management System ◽  
Real Time Control ◽  
Time Control ◽  
Color Mixing ◽  
Different Color ◽  
And Control

In this paper, a smart green energy management system based on DMX512 protocol that is established by United States Institute for Theatre Technology (USITT) is proposed. The end user can monitor and control the LED lamps and electric powers according to the information received from the sensors by means of the channels of DMX512. In addition, the different color lights including red, green, and blue lights can be achieved by means of the color-mixing methods of red, green, and blue LEDs based on photometry theory. As a matter of fact, there have been attracted much attention on the mobile devices in recent years. In the study, a mobile device with an Android platform is used to control the electric power and LED lamps according to the information received from the ZigBee module immediately. However, the smart green energy management system based on DMX512 protocol has some merits, such as in real-time control, easy operation, low cost, etc.

Global Positioning System (GPS): A Low-Cost Velocity Sensor for Correcting Inertial Sensor Errors on Ground Vehicles

2004 ◽  
Vol 126 (2) ◽  
pp. 255-264 ◽  
Author(s):  
David M. Bevly
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Positioning System ◽  
Ground Vehicles ◽  
Velocity Sensor ◽  
Global Positioning ◽  
Sensor Errors

This paper demonstrates the ability of a standard low-cost Global Positioning System (GPS) receiver to reduce errors inherent in low-cost accelerometers and rate gyroscopes used on ground vehicles. Specifically GPS velocity is used to obtain vehicle course, velocity, and road grade, as well as to correct inertial sensors errors, providing accurate longitudinal and lateral acceleration, and pitch, roll, and yaw angular velocities. Additionally, it is shown that transient changes in sideslip (or lateral velocity), roll, and pitch angles can be measured. The method utilizes GPS velocity measurements to determine the inertial sensor errors using a kinematic Kalman Filter estimator. Simple models of the inertial sensors, which take into account the sensor noise and bias drift properties, are developed and used to design the estimator. Based on the characteristics of low-cost GPS receivers and IMU sensors, this paper presents the achievable performance of the combined system using the covariance analysis from the Kalman filter. Subsequent simulations and experiments validate both the error analysis and the methodology for utilizing GPS as a velocity sensor for correcting low-cost inertial sensor errors and providing critical vehicle state measurements.

Global Positioning System Denied Navigation of Autonomous Parafoil Systems Using Beacon Measurements From a Single Location

2017 ◽  
Vol 140 (4) ◽  
Author(s):  
Martin R. Cacan ◽  
Mark Costello ◽  
Edward Scheuermann
Keyword(s):  
Global Positioning System ◽  
Autonomous Vehicles ◽  
Positioning System ◽  
Signal Jamming ◽  
Package Delivery ◽  
Single Location ◽  
Global Positioning ◽  
Urban Terrain ◽  
Single Radio ◽  
And Control

Precision-guided airdrop systems have shown considerable accuracy improvements over more widely used unguided systems through high-quality position, velocity, and time feedback provided by global positioning system (GPS). These systems, like many autonomous vehicles, have become solely dependent on GPS to conduct mission operations. This necessity makes airdrop systems susceptible to GPS blackout in mountainous or urban terrain due to multipathing issues or from signal jamming in active military zones. This work overcomes loss of GPS through an analysis of guidance, navigation and control (GNC) capabilities using a single radio frequency (RF) beacon located at the target. Such a device can be deployed at the target by ground crew on site to retrieve package delivery. Two novel GNC algorithms are presented, which use either range from or direction to a RF beacon. Simulation and experimental flight testing results indicated that beacon-based methods can achieve similar results as GPS-based methods. This technology provides a simple and elegant solution to GPS blackout with best method studied showing only a 21% decrease in landing accuracy in comparison to GPS-based methods.

Adaptive Kalman Filter for INS/GPS Integrated Navigation System

2013 ◽  
Vol 336-338 ◽  
pp. 332-335 ◽  
Author(s):  
Tian Lai Xu
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Navigation System ◽  
A Priori ◽  
Noise Characteristic ◽  
Integrated Navigation ◽  
Adaptive Kalman Filter ◽  
Integrated Navigation System ◽  
Global Positioning ◽  
Priori Information

Inertial Navigation System (INS) and Global Positioning System (GPS) are commonly integrated to overcomes each systems inadequacies and provide an accurate navigation solution. The integration of INS and GPS is usually achieved using a Kalman filter. The accuracy of INS/GPS deteriorates in condition that a priori information used in Kalman filter does not accord with the actual environmental conditions. To address this problem, an improved Sage-Husa filter is presented. In this method, the measurement noise characteristic is adjusted if and only if filtering abnormality exists, avoiding filter instability and reducing computational burden caused by adjusting noise characteristic too frequently in Sage-Husa filter. Simulations in INS/GPS integrated navigation showed improvement in positioning accuracy.

Fuzzy adaptive Kalman filter for the drive system with an elastic coupling

2013 ◽  
Vol 62 (2) ◽  
pp. 251-265 ◽  
Author(s):  
Piotr J. Serkies ◽  
Krzysztof Szabat
Keyword(s):  
Kalman Filter ◽  
Control Structure ◽  
Experimental Tests ◽  
Drive System ◽  
Adaptive Kalman Filter ◽  
State Matrix ◽  
State Controller ◽  
Kalman Gain ◽  
Robust Adaptive ◽  
Fuzzy Adaptive

Abstract In the paper issues related to the design of a robust adaptive fuzzy estimator for a drive system with a flexible joint is presented. The proposed estimator ensures variable Kalman gain (based on the Mahalanobis distance) as well as the estimation of the system parameters (based on the fuzzy system). The obtained value of the time constant of the load machine is used to change the values in the system state matrix and to retune the parameters of the state controller. The proposed control structure (fuzzy Kalman filter and adaptive state controller) is investigated in simulation and experimental tests.

scholarly journals An On-Line Low-Cost Irradiance Monitoring Network with Sub-Second Sampling Adapted to Small-Scale PV Systems

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3405 ◽  
Author(s):  
Manuel Espinosa-Gavira ◽  
Agustín Agüera-Pérez ◽  
Juan González de la Rosa ◽  
José Palomares-Salas ◽  
José Sierra-Fernández
Keyword(s):  
Sensor Networks ◽  
Low Cost ◽  
Monitoring Network ◽  
Absolute Error ◽  
Small Scale ◽  
Photovoltaic Systems ◽  
Real Time Control ◽  
Short Term ◽  
Time Control ◽  
Cloud Motion

Very short-term solar forecasts are gaining interest for their application on real-time control of photovoltaic systems. These forecasts are intimately related to the cloud motion that produce variations of the irradiance field on scales of seconds and meters, thus particularly impacting in small photovoltaic systems. Very short-term forecast models must be supported by updated information of the local irradiance field, and solar sensor networks are positioning as the more direct way to obtain these data. The development of solar sensor networks adapted to small-scale systems as microgrids is subject to specific requirements: high updating frequency, high density of measurement points and low investment. This paper proposes a wireless sensor network able to provide snapshots of the irradiance field with an updating frequency of 2 Hz. The network comprised 16 motes regularly distributed over an area of 15 m × 15 m (4 motes × 4 motes, minimum intersensor distance of 5 m). The irradiance values were estimated from illuminance measurements acquired by lux-meters in the network motes. The estimated irradiances were validated with measurements of a secondary standard pyranometer obtaining a mean absolute error of 24.4 W/m 2 and a standard deviation of 36.1 W/m 2 . The network was able to capture the cloud motion and the main features of the irradiance field even with the reduced dimensions of the monitoring area. These results and the low-cost of the measurement devices indicate that this concept of solar sensor networks would be appropriate not only for photovoltaic plants in the range of MW, but also for smaller systems such as the ones installed in microgrids.

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