An autonomous rice transplanter guided by global positioning system and inertial measurement unit

2009 ◽  
Vol 26 (6-7) ◽  
pp. 537-548 ◽  
Author(s):  
Yoshisada Nagasaka ◽  
Hidefumi Saito ◽  
Katsuhiko Tamaki ◽  
Masahiro Seki ◽  
Kyo Kobayashi ◽  
...  
Keyword(s):  
Global Positioning System ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Positioning System ◽  
Inertial Measurement ◽  
Global Positioning

Cartesian Control for the Inertial Measurement Unit Calibration Platform

2000 ◽  
Author(s):  
John J. Hall ◽  
Robert L. Williams ◽  
Frank van Graas
Keyword(s):  
Global Positioning System ◽  
Inertial Measurement Unit ◽  
Vertical Motion ◽  
High Accuracy ◽  
Measurement Unit ◽  
Positioning System ◽  
Ohio University ◽  
Inertial Measurement ◽  
Gps Technology ◽  
Global Positioning

Abstract The Department of Mechanical Engineering and the Avionics Engineering Center at Ohio University are developing an electromechanical system for the calibration of an inertial measurement unit (IMU) using global positioning system (GPS) antennas. The GPS antennas and IMU are mounted to a common platform to be oriented in the angular roll, pitch, and yaw motions. Vertical motion is also included to test the systems in a vibrational manner. A four-dof system based on the parallel Carpal Wrist is under development for this task. High-accuracy positioning is not required from the platform since the GPS technology provides absolute positioning for the IMU calibration process.

scholarly journals Vehicle location measurement method for radio-shadow area through iBeacon message

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881257
Author(s):  
ChoonSung Nam ◽  
Dong-Ryeol Shin
Keyword(s):  
Global Positioning System ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Positioning System ◽  
Inertial Measurement ◽  
Location Data ◽  
Shadow Area ◽  
Global Positioning ◽  
Vehicle Location ◽  
Roadside Unit

Information communication technology related vehicle services need to support location and the transmission of communication and traffic information between vehicles, or between vehicles and infrastructure. In particular, the technology for the measurement of the accurate location of a vehicle is dependent on location-determination technology like Global Positioning System, and this technology is very important for vehicle driving and location services. If, however, a vehicle is in a Global Positioning System radio-shadow area, neither a Global Positioning System nor a Differential Global Positioning System can accurately measure the corresponding location because of a high error rate caused by the shadowing intervention. Even an Inertial Measurement Unit could provide inaccurate location data due to sensor drift faults around corners and traffic-road speed dumps. Vehicles, therefore, need an absolute location to prevent the provision of inaccurate vehicle-location data that is due to radio-shadow areas and relational Inertial Measurement Unit positions. To achieve this, we assume that vehicle-to-infrastructure communication is possible between a vehicle and roadside unit in Vehicular Ad hoc Networks. We used iBeacon at the roadside unit and revised its Universally Unique Identifier so that it generates absolute Global Positioning System location data; that is, moving vehicles can receive absolute Global Positioning System data from the roadside unit-based iBeacon. We compared the proposed method with current Global Positioning System and Inertial Measurement Unit systems for the following two cases: one with a radio-shadow area and one without. We proved that the proposed method generates location data that are more accurate than those of the other methods.

Three-Axis Gimbal Surveillance Algorithms for Use in Small UAS

2008 ◽  
Author(s):  
Jaganathan Ranganathan ◽  
William H. Semke
Keyword(s):  
Coordinate System ◽  
Global Positioning System ◽  
Inertial Measurement Unit ◽  
Target Location ◽  
Measurement Unit ◽  
Positioning System ◽  
Inertial Measurement ◽  
University Of North Dakota ◽  
Global Positioning ◽  
Accurate Position

An active three-axis gimbal system is developed to allow small fixed wing Unmanned Aircraft Systems (UAS) platforms to estimate accurate position information by pointing at a target and also to track a known target location. Specific targets vary from a stationary point on the ground to aircraft in the national airspace. The payload developed to accomplish this at the University of North Dakota is the Surveillance by University of North Dakota Observational Gimbal (SUNDOG). This paper will focus on a novel, nonlinear closed form analytical algorithm developed to calculate the exact rotation angles for a three-axis gimbal system to point a digital imaging sensor at a target, as well as how to estimate accurate position of a target by using the pointing angles of a three-axis gimbal system. A kinematic analysis is done on a three-axis gimbal system to get the appropriate model of gimbal rotations in order to point at a certain location on the ground. The mathematical model includes an inertial coordinate system that has coordinates fixed to the Earth, a coordinate system that is body-fixed to the aircraft, and a third coordinate system that is fixed to the gimbal. Therefore, multiple three-dimensional transformations are required to accurately provide the necessary pointing angles to the gimbal system. The autonomous control system uses Global Positioning System (GPS), Inertial Measurement Unit (IMU), and other sensor data to estimate position and attitude during flight. Since the algorithm is entirely based on Inertial Measurement Unit (IMU) and Global Positioning System (GPS) device inputs, any error from these devices cause offset in the target location. Hence, an error analysis is carried out to find the offset distance and the operating range of the algorithm. The main advantage obtained in the three-axis gimbal system is that the orientation of the image will always be aligned in a specified direction for effective interpretation. The closed form expressions to the non-linear transformations provide simple solutions easily programmed without large computational expense. Experimental work will be carried out in a controlled environment and in flight testing to show the autonomous tracking ability of the gimbal system. Simulation and experimental data illustrating the effectiveness of the surveillance algorithms is presented.

Adaptive Redundant Inertial Measurement Unit/Global Positioning System Integration Filter Structure for Fault Tolerant Navigation

2013 ◽  
Vol 136 (2) ◽  
Author(s):  
Seong Yun Cho ◽  
Hyung Keun Lee ◽  
Chan Gook Park
Keyword(s):  
Global Positioning System ◽  
Adaptive Filter ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Positioning System ◽  
Body Frame ◽  
Error Models ◽  
Inertial Measurement ◽  
Filter Structure ◽  
Global Positioning

This paper proposes a new adaptive filter structure containing two subfilters, a fault detection and isolation (FDI) filter, and a main-filter for redundant inertial measurement unit (RIMU)/global positioning system (GPS) integrated navigation system tolerant toward faults of inertial sensors. The purpose of subfilters is compensation of sensor level bias (SLB) of the RIMU during in-flight alignment for successful FDI processing. Also, that of the main-filter is providing of error compensated navigation solution based on the good FDI result. To achieve these purposes, two coordinate frames, sub-body frame (SBF) and master-body frame (MBF), are defined first. Then, two different error models for the RIMU are formulated in the SBF for sensor level compensation and in the MBF for module level compensation, respectively. Based on the formulated error models, an adaptive filter structure is designed. Some numerical simulations are performed to validate the performance of the proposed adaptive filter structure.

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