Approaches to design of temporary blackout handling capabilities and an evaluation with a real-time tightly coupled network testbed

2002 ◽  
Author(s):  
K.H. Kim ◽  
W.J. Guan ◽  
A. Damm ◽  
J.A. Rohr
Keyword(s):  
Real Time ◽  
Tightly Coupled

Concurrency Control in Real-Time E-Collaboration Systems

2009 ◽  
pp. 211-218
Author(s):  
Wenbing Zhao
Keyword(s):  
User Interface ◽  
System Design ◽  
Real Time ◽  
Graphical User Interface ◽  
Concurrency Control ◽  
Wide Area ◽  
Wide Area Networks ◽  
System State ◽  
Decentralized System ◽  
Tightly Coupled

For all e-collaboration systems, some degree of concurrency control is needed so that two people do not step on each other’s foot. The demand for good concurrency control is especially high for the tightly coupled, real-time e-collaboration systems. Such systems require quick responses to user’s actions, and typically require a WYSIWIS (what you see is what I see) graphical user interface (Ellis, Gibbs, & Rein, 1991). This requirement, together with the fact that users are often separated geographically across wide-area networks, favors a decentralized system design where the system state is replicated at each user’s site. This places further challenges on the design of concurrency control for these systems.

Enabling robust and accurate navigation for UAVs using real-time GNSS precise point positioning and IMU integration

2020 ◽  
Vol 125 (1283) ◽  
pp. 87-108
Author(s):  
C. Chi ◽  
X. Zhan ◽  
S. Wang ◽  
Y. Zhai
Keyword(s):  
Real Time ◽  
Precise Point Positioning ◽  
Three Dimensional ◽  
Coupled System ◽  
Measurement Unit ◽  
Satellite Geometry ◽  
Tightly Coupled ◽  
Point Positioning ◽  
Gnss Measurements ◽  
The One

ABSTRACTAccurate navigation is required in many Unmanned Aerial Vehicle (UAV) applications. In recent years, GNSS Precise Point Positioning (PPP) has been recognised as an efficient approach for providing precise positioning services. In contrast to the widely used Real-Time Kinematic (RTK), PPP is independent of reference stations, which greatly broadens its scope of application. However, the accuracy and reliability of PPP can be significantly decreased by poor GNSS satellite geometry and outage. In response, a real-time four-constellation GNSS PPP is applied to improve the geometry in this work, and PPP is tightly coupled with an Inertial Measurement Unit (IMU) to smooth the position and velocity output, thus improving the robustness of the navigation solution. Experimental flight tests are carried out using a UAV in an open-sky area, and GNSS-challenged environments are simulated. The results show that the four-constellation GNSS PPP/IMU integration reduces the Root-Mean-Square (RMS) Three-Dimensional (3D) positioning and velocity error by 76.4% and 67.1%, respectively, in open sky with respect to the one-GNSS PPP. Under scenarios where GNSS measurements are insufficient, the coupled system can still provide continuous solutions. Moreover, the coupled PPP/IMU system can also maintain the convergence of PPP during GNSS-challenged periods and can greatly shorten the re-convergence period of PPP when the UAV returns to the open sky.

scholarly journals Visual-Inertial Odometry of Smartphone under Manhattan World

2020 ◽  
Vol 12 (22) ◽  
pp. 3818
Author(s):  
YuAn Wang ◽  
Liang Chen ◽  
Peng Wei ◽  
XiangChen Lu
Keyword(s):  
Mobile Phone ◽  
Structural Feature ◽  
Real Time ◽  
Structural Features ◽  
Feature Extraction Method ◽  
The Public ◽  
Window Method ◽  
Tightly Coupled ◽  
Sliding Window Method ◽  
Public Datasets

Based on the hypothesis of the Manhattan world, we propose a tightly-coupled monocular visual-inertial odometry (VIO) system that combines structural features with point features and can run on a mobile phone in real-time. The back-end optimization is based on the sliding window method to improve computing efficiency. As the Manhattan world is abundant in the man-made environment, this regular world can use structural features to encode the orthogonality and parallelism concealed in the building to eliminate the accumulated rotation error. We define a structural feature as an orthogonal basis composed of three orthogonal vanishing points in the Manhattan world. Meanwhile, to extract structural features in real-time on the mobile phone, we propose a fast structural feature extraction method based on the known vertical dominant direction. Our experiments on the public datasets and self-collected dataset show that our system is superior to most existing open-source systems, especially in the situations where the images are texture-less, dark, and blurry.

A covariance shaping filtering method for tightly-coupled MIMU/GNSS of UAV

2019 ◽  
Vol 91 (10) ◽  
pp. 1257-1267 ◽  
Author(s):  
Bin Liu ◽  
Jiangtao Xu ◽  
Bangsheng Fu ◽  
Yong Hao ◽  
Tianyu An
Keyword(s):  
Real Time ◽  
Kalman Filtering ◽  
Adaptive Filter ◽  
Dynamic Environments ◽  
Content Type ◽  
Filtering Method ◽  
Time Performance ◽  
Dual Channel ◽  
Tightly Coupled ◽  
Parallel Filter

Purpose Regarding the important roles of accuracy and robustness of tightly-coupled micro inertial measurement unit (MIMU)/global navigation satellite system (GNSS) for unmanned aerial vehicle (UAV). This study aims to explore the efficient method to improve the real-time performance of the sensors. Design/methodology/approach A covariance shaping adaptive Kalman filtering method is developed. For optimal performance of multiple gyros and accelerometers, a distribution coefficient of precision is defined and the data fusion least square method is applied with fault detection and identification using the singular value decomposition. A dual channel parallel filter scheme with a covariance shaping adaptive filter is proposed. Findings Hardware-in-the-loop numerical simulation was adopted, the results indicate that the gain of the covariance shaping adaptive filter is self-tuning by changing covariance weighting factor, which is calculated by minimizing the cost function of Frobenius norm. With the improved method, the positioning accuracy with tightly-coupled MIMU/GNSS of the adaptive Kalman filter is increased obviously. Practical implications The method of covariance shaping adaptive Kalman filtering is efficient to improve the accuracy and robustness of tightly-coupled MIMU/GNSS for UAV in complex and dynamic environments and has great value for engineering applications. Originality/value A covariance shaping adaptive Kalman filtering method is presented and a novel dual channel parallel filter scheme with a covariance shaping adaptive filter is proposed, to improve the real-time performance in complex and dynamic environments.

An Amoeboid Locomotion That Exploits Real-Time Tunable Springs and Law of Conservation of Protoplasmic Mass

2008 ◽  
Vol 20 (3) ◽  
pp. 449-455
Author(s):  
Takuya Umedachi ◽  
◽  
Taichi Kitamura ◽  
Akio Ishiguro
Keyword(s):  
Control System ◽  
Mechanical System ◽  
Real Time ◽  
Mechanical Systems ◽  
The Body ◽  
Amoeboid Locomotion ◽  
Central Controller ◽  
Tightly Coupled ◽  
Computational Offloading ◽  
Embodied Agent

The control and mechanical systems of an embodied agent should be tightly coupled so as to emerge useful functionalities such as adaptivity. This indicates that the mechanical system as well as the control system should be responsible for a certain amount of computation for generating the behavior. However, there still leaves much to be understood about how such “computational offloading” from the control system to the mechanical system can be achieved. In order to intensively investigate this, here we particularly focus on the “softness” of the body, and show how the computational offloading derived from this property is exploited to simplify the control system and to increase the degree of adaptivity. To this end, we employ a two-dimensional amoeboid robot as a practical example, consisting of incompressive fluid (i.e. protoplasm) covered with an outer skin composed of a network of real-time tunable springs. Preliminary simulation results show that the exploitation of the “long-distant interaction” stemming from “the law of conservation of protoplasmic mass” allows us to simplify the control mechanism; and that adaptive amoeboid locomotion can be realized without the need of a central controller. The results obtained are expected to shed light on how control and mechanical systems should be coupled, and what the “brain-body-interaction” carefully designed brings to the resulting behavior.

Performance evaluation of real-time tightly-coupled GNSS PPP/MEMS-based inertial integration using an improved robust adaptive Kalman filter

2020 ◽  
Vol 14 (4) ◽  
pp. 413-430
Author(s):  
Abdelsatar Elmezayen ◽  
Ahmed El-Rabbany
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Integrated System ◽  
Adaptive Kalman Filter ◽  
Integration Algorithm ◽  
Positioning Accuracy ◽  
Precise Positioning ◽  
The Real ◽  
Tightly Coupled ◽  
Robust Adaptive

AbstractTypically, the extended Kalman filter (EKF) is used for tightly-coupled (TC) integration of multi-constellation GNSS PPP and micro-electro-mechanical system (MEMS) inertial navigation system (INS) to provide precise positioning, velocity, and attitude solutions for ground vehicles. However, the obtained solution will generally be affected by both of the GNSS measurement outliers and the inaccurate modeling of the system dynamic. In this paper, an improved robust adaptive Kalman filter (IRKF) is adopted and used to overcome the effect of the measurement outliers and dynamic model errors on the obtained integrated solution. A real-time IRKF-based TC GPS+Galileo PPP/MEMS-based INS integration algorithm is developed to provide precise positioning and attitude solutions. The pre-saved real-time orbit and clock products from the Centre National d’Etudes Spatials (CNES) are used to simulate the real-time scenario. The performance of the real-time IRKF-based TC GNSS PPP/INS integrated system is assessed under open sky environment, and both of simulated partial and complete GNSS outages through two ground vehicular field trials. It is shown that the real-time TC GNSS PPP/INS integration through the IRKF achieves centimeter-level positioning accuracy under open sky environments and decimeter-level positioning accuracy under GNSS outages that range from 10 to 60 seconds. In addition, the use of IRKF improves the positioning accuracy and enhances the convergence of the integrated solution in comparison with the EKF. Furthermore, the IRKF-based integrated system achieves attitude accuracy of 0.052°, 0.048°, and 0.165° for pitch, roll, and azimuth angles, respectively. This represents improvement of 44 %, 48 %, and 36 % for the pitch, roll, and azimuth angles, respectively, in comparison with the EKF-based counterpart.

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