DATA ASSOCIATION OF RF-VSLAM FOR OCEAN OBSERVATION USING BLIMP

2015 ◽  
Vol 74 (9) ◽  
Author(s):  
Herdawatie Abdul Kadir ◽  
M. R. Arshad
Keyword(s):  
Data Association ◽  
Observation System ◽  
Features Selection ◽  
State Estimator ◽  
Dynamic Changes ◽  
Localization And Mapping ◽  
Aerial Vehicle ◽  
Ocean Gyres ◽  
Correct Association ◽  
Selection Of

This paper describes a selection of features for potential landmarks for ocean observation system using radio frequency visual simultaneous localization and mapping (RF-VSLAM) framework. Due to dynamic changes of the ocean surface caused by the ocean gyres, the features selection is difficult. Therefore, the tendency for vehicles to drift is high. As a solution, we introduced the beacons as an anchor node as an aid to correct the navigation and improve data association. We investigated the data association stage of the RF-VSLAM system which improved the state estimator for the aerial vehicle. The goal is to produce a correct association to the landmarks, since wrong data association will produce inaccurate maps. The points features were extracted from a monocular camera using SIFT as detector and descriptor. The experimental data of the dynamic changes of water surface has been evaluated. The result showed that the data association method was able to produce correct and accurate landmarks selection.  

Comparison of Controllers for a UAV with Integral Effect and Kalman Estimator: By Bessel Polynomials and LQR

2013 ◽  
Vol 436 ◽  
pp. 54-60 ◽  
Author(s):  
Wenceslao Eduardo Rodríguez ◽  
Ramiro Ibarra ◽  
Gerardo Romero ◽  
David Lara ◽  
Jaime Arredondo ◽  
...  
Keyword(s):  
Steady State ◽  
Pole Placement ◽  
Control Technique ◽  
State Estimator ◽  
Control Laws ◽  
State Response ◽  
Integral Effect ◽  
Aerial Vehicle ◽  
Pole Placement Controller ◽  
Selection Of

This paper presents the development of two different control techniques as an approach having to remove steady-state error present in the response of attitude of a mini unmanned aerial vehicle. A problem that arises when performing pole placement controller is the selection of the poles, the Bessel approximation allows the selection of the eigenvalues in function to a specified response time for a feedback pole placement controller and state estimator (observer). On the other hand presents an optimal control technique combined with Kalman filter to estimate the state affected by perturbations in the system, both cases using the integral effect to eliminate the steady state error.These two control laws has the property of responding to a desired response according to a time or state response desired.

Comparison of Controllers for a UAV Type Quadrotor: Feedback Control by Bessel´s Polynomials and LQR with Kalman Filter

2014 ◽  
Vol 555 ◽  
pp. 40-48 ◽  
Author(s):  
Wenceslao Eduardo Rodríguez ◽  
Ramiro Ibarra ◽  
Gerardo Romero ◽  
David Lara
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
Pole Placement ◽  
Control Technique ◽  
State Estimator ◽  
Integral Effect ◽  
Aerial Vehicle ◽  
Pole Placement Controller ◽  
Selection Of ◽  
State Error

This paper presents the development of two different control techniques as an approach having to remove steady-state error present in the response of attitude of a mini unmanned aerial vehicle of four rotor model. The Bessel approximation allows the selection of the eigenvalues in function to a specified response time for a feedback pole placement controller and state estimator. On the other hand presents an optimal control technique combined with Kalman filter to estimate the state affected by perturbations in the system, both cases using the integral effect to eliminate the steady state error.

scholarly journals An Optimized Tightly-Coupled VIO Design on the Basis of the Fused Point and Line Features for Patrol Robot Navigation

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2004 ◽  
Author(s):  
Linlin Xia ◽  
Qingyu Meng ◽  
Deru Chi ◽  
Bo Meng ◽  
Hanrui Yang
Keyword(s):  
Global Positioning System ◽  
Inertial Measurement Unit ◽  
Feature Matching ◽  
Robot Navigation ◽  
Measurement Unit ◽  
State Estimator ◽  
Localization And Mapping ◽  
Global Positioning ◽  
Tightly Coupled ◽  
Association State

The development and maturation of simultaneous localization and mapping (SLAM) in robotics opens the door to the application of a visual inertial odometry (VIO) to the robot navigation system. For a patrol robot with no available Global Positioning System (GPS) support, the embedded VIO components, which are generally composed of an Inertial Measurement Unit (IMU) and a camera, fuse the inertial recursion with SLAM calculation tasks, and enable the robot to estimate its location within a map. The highlights of the optimized VIO design lie in the simplified VIO initialization strategy as well as the fused point and line feature-matching based method for efficient pose estimates in the front-end. With a tightly-coupled VIO anatomy, the system state is explicitly expressed in a vector and further estimated by the state estimator. The consequent problems associated with the data association, state optimization, sliding window and timestamp alignment in the back-end are discussed in detail. The dataset tests and real substation scene tests are conducted, and the experimental results indicate that the proposed VIO can realize the accurate pose estimation with a favorable initializing efficiency and eminent map representations as expected in concerned environments. The proposed VIO design can therefore be recognized as a preferred tool reference for a class of visual and inertial SLAM application domains preceded by no external location reference support hypothesis.

Dynamic Characterization of Brushless DC Motors and Propellers for Flight Applications

2017 ◽  
Vol 05 (03) ◽  
pp. 159-167 ◽  
Author(s):  
Dominic Muzar ◽  
Eric Lanteigne ◽  
Justin McLeod
Keyword(s):  
Simple Model ◽  
Brushless Dc Motor ◽  
Dynamic Characterization ◽  
Brushless Dc Motors ◽  
Brushless Dc ◽  
Dc Motors ◽  
Simple Motor ◽  
Aerial Vehicle ◽  
Selection Of

Although there exist a number of accurate unmanned aerial vehicle (UAV) thruster models, these models require the precise measurements of several motor and propeller characteristics. This paper presents a simple motor and propeller model that relies solely upon data provided by manufacturers. The model is validated by comparing theoretical motor and propeller behavior to experimental results obtained from thrust tests in a wind tunnel. The objective is to provide an accurate yet simple model to facilitate the selection of appropriate brushless DC motor and propeller combinations for flight applications.

scholarly journals Selection of homogeneous zones of agricultural field for laying of experiments using unmanned aerial vehicle

2018 ◽  
Vol 14 (2) ◽  
pp. 145-150
Author(s):  
Vladimir M. Bure ◽  
◽  
Evgenii P. Mitrofanov ◽  
Olga A. Mitrofanova ◽  
Aleksei F. Petrushin ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Agricultural Field ◽  
Aerial Vehicle ◽  
Selection Of

Autonomous Unmanned Aerial Vehicle for Post-Disaster Management With Cognitive Radio Communication

2021 ◽  
Vol 12 (1) ◽  
pp. 29-52
Author(s):  
Raja Guru R. ◽  
Naresh Kumar P.
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Processing Technique ◽  
High Accuracy ◽  
Image Processing Technique ◽  
Radio Communication ◽  
Dynamic Changes ◽  
General Graph ◽  
Vision Based Navigation ◽  
Aerial Vehicle ◽  
Post Disaster

Unmanned aerial vehicles (UAV) play a significant role in finding victims affected in the post-disaster zone, where a man cannot risk his life under a critical condition of the disaster environment. The proposed design incorporates autonomous vision-based navigation through the disaster environment based on general graph theory with dynamic changes on the length between two or multiple nodes, where a node is a pathway. Camera fixed on it continuously captures the surrounding footage, processing it frame by frame on-site using image processing technique based on a SOC. Identifies victims in the zone and the pathways available for traversal. UAV uses an ultrasonic rangefinder to avoid collision with obstacles. The system alerts the rescue team if any victim detected and transmits the frames using CRN to the off-site console. UAV learns navigation policy that achieves high accuracy in real-time environments; communication using CRN is uninterrupted and useful during such emergencies.

scholarly journals P240 Can ultrasound alone predict the need to treat ACPA positive individuals without synovitis?

Rheumatology ◽  
2020 ◽  
Vol 59 (Supplement_2) ◽  
Author(s):  
Laurence M Duquenne ◽  
Kulveer Mankia ◽  
Leticia Garcia Montoya ◽  
Andrea Di Matteo ◽  
Jacqueline Nam ◽  
...  
Keyword(s):  
At Risk ◽  
Predictive Value ◽  
Cox Regression ◽  
Clinical Symptoms ◽  
Power Doppler ◽  
Musculoskeletal Symptoms ◽  
Features Selection ◽  
Moderate Sensitivity ◽  
Us Scan ◽  
Selection Of

Abstract Background In anti-cyclic citrullinated peptide antibody-positive (ACPA+) individuals without clinical synovitis (at-risk), to define the critical ultrasound (US) features sufficiently predictive for inflammatory arthritis (IA) to enable logical initiation of therapy. Methods In a single centre prospective cohort, at risk ACPA+ individuals with a new musculoskeletal symptoms underwent an US scan of 38 joints and 18 tendons at first visit. The predictive value of US abnormalities (Power Doppler (PD), Grey Scale (GS), erosion or tenosynovitis (TSV)) for progression to IA was analysed and the best predictive joints determined by Multivariable Cox Regression, adjusted for confounders. The US results were combined with clinical symptoms/findings to produce predictive models. Results Consecutive at-risk ACPA+ individuals (n = 457, mean age 50.3 years old, 74.2% women) were followed up for median of 15.4 months (range 0.1-127.4), a complete dataset with follow-up of at least 6 months was available for 319 of them. 135 (29.5%) developed IA after a median of 11.3 months (range 0.1-111.7). The negative predictive value of a US scan without any abnormality was 82%. In multivariable Cox regression, both PD and TSV were predictive of progression, with respectively hazard ratios of 1.2 (9=0.026) and 1.13 (p = 0.025). All US abnormalities had a high specificity (spec) but only moderate sensitivity (sens), PD was the most specific with a spec/sens of 0.94/0.23, followed by TSV with a spec/sens of 0.91/0.26 but the best area under the curve (AUC) of 0.599 (P = 0.0015). The addition ACPA titre (high compared to low), but not GS, improved spec/sens up to 0.92/0.34 and AUC to 0.964 (p < 0.001). A selection of US and clinical data of 14 joints also improved prediction, with an AUC of 0.670 (p < 0.001) and a spec/sens of 0.65/0.62. A selection of the 34 most predictive features reached the same sens/spec as the ACR/EULAR 2010 classification criteria for RA, showing a spec/sens of 0.80/0.56. Conclusion In at-risk ACPA+ individuals, the presence of sub-clinical US abnormalities are highly specific for progression to IA. The only moderate sensitivity can be improved by using joints or features selection in combination with clinical examination. These results are the first step in providing guidance for which at-risk ACPA+ individuals to treat. Disclosures L.M. Duquenne None. K. Mankia None. L. Garcia Montoya None. A. Di Matteo None. J. Nam None. P. Emery None.

scholarly journals A Machine Learning Method for Vision-Based Unmanned Aerial Vehicle Systems to Understand Unknown Environments

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3245
Author(s):  
Tianyao Zhang ◽  
Xiaoguang Hu ◽  
Jin Xiao ◽  
Guofeng Zhang
Keyword(s):  
Machine Learning ◽  
Visual Servoing ◽  
Detection System ◽  
Detection Accuracy ◽  
Unknown Environment ◽  
Unknown Environments ◽  
Localization And Mapping ◽  
Computational Speed ◽  
Aerial Vehicle ◽  
High Level

What makes unmanned aerial vehicles (UAVs) intelligent is their capability of sensing and understanding new unknown environments. Some studies utilize computer vision algorithms like Visual Simultaneous Localization and Mapping (VSLAM) and Visual Odometry (VO) to sense the environment for pose estimation, obstacles avoidance and visual servoing. However, understanding the new environment (i.e., make the UAV recognize generic objects) is still an essential scientific problem that lacks a solution. Therefore, this paper takes a step to understand the items in an unknown environment. The aim of this research is to enable the UAV with basic understanding capability for a high-level UAV flock application in the future. Specially, firstly, the proposed understanding method combines machine learning and traditional algorithm to understand the unknown environment through RGB images; secondly, the You Only Look Once (YOLO) object detection system is integrated (based on TensorFlow) in a smartphone to perceive the position and category of 80 classes of objects in the images; thirdly, the method makes the UAV more intelligent and liberates the operator from labor; fourthly, detection accuracy and latency in working condition are quantitatively evaluated, and properties of generality (can be used in various platforms), transportability (easily deployed from one platform to another) and scalability (easily updated and maintained) for UAV flocks are qualitatively discussed. The experiments suggest that the method has enough accuracy to recognize various objects with high computational speed, and excellent properties of generality, transportability and scalability.

scholarly journals Simulation of UAV Systems

10.14311/754 ◽  
2005 ◽  
Vol 45 (4) ◽  
Author(s):  
P. Kaňovský ◽  
L. Smrcek ◽  
C. Goodchild
Keyword(s):  
Dynamical System ◽  
Unmanned Aerial Vehicle ◽  
Low Cost ◽  
Design Tool ◽  
Inertial System ◽  
Principal Function ◽  
Low Weight ◽  
High Bandwidth ◽  
Aerial Vehicle ◽  
Selection Of

The study described in this paper deals with the issue of a design tool for the autopilot of an Unmanned Aerial Vehicle (UAV) and the selection of the airdata and inertial system sensors. This project was processed in cooperation with VTUL a PVO o.z. [1]. The feature that distinguishes the autopilot requirements of a UAV (Figs. 1, 7, 8) from the flight systems of conventional manned aircraft is the paradox of controlling a high bandwidth dynamical system using sensors that are in harmony with the low cost low weight objectives that UAV designs are often expected to achieve. The principal function of the autopilot is flight stability, which establishes the UAV as a stable airborne platform that can operate at a precisely defined height. The main sensor for providing this height information is a barometric altimeter. The solution to the UAV autopilot design was realised with simulations using the facilities of Matlab® and in particular Simulink®[2]. 

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