Power line field sensing to support autonomous navigation of small unmanned aerial vehicles

2013 ◽  
Author(s):  
John Matthews ◽  
Leonid Bukshpun ◽  
Ranjit Pradhan
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Autonomous Navigation ◽  
Power Line ◽  
Line Field ◽  
Aerial Vehicles ◽  
Field Sensing

Image matching applied to autonomous navigation of unmanned aerial vehicles

2016 ◽  
Vol 6 (4) ◽  
pp. 205 ◽  
Author(s):  
C.A.O. Silva ◽  
G.A.M. Goltz ◽  
E.H. Shiguemori ◽  
C.L. De Castro ◽  
H.F. De C. Velho ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Image Matching ◽  
Autonomous Navigation ◽  
Aerial Vehicles

scholarly journals Development of Radio-Frequency Sensor Wake-Up with Unmanned Aerial Vehicles as an Aerial Gateway

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1047 ◽  
Author(s):  
Jianfei Chen ◽  
Zhaohua Dai ◽  
ZhiQiang Chen
Keyword(s):  
Energy Efficiency ◽  
Radio Frequency ◽  
Unmanned Aerial Vehicles ◽  
Autonomous Navigation ◽  
Engineering Practice ◽  
Opportunistic Sensing ◽  
Aerial Vehicles ◽  
Frequency Sensor ◽  
Sensing Platform ◽  
Radio Frequency Sensor

The advent of autonomous navigation, positioning, and general robotics technologies has enabled the improvement of small to miniature-sized unmanned aerial vehicles (UAVs, or ‘drones’) and their wide uses in engineering practice. Recent research endeavors further envision a systematic integration of aerial drones and traditional contact-based or ground-based sensors, leading to an aerial–ground wireless sensor network (AG-WSN), in which the UAV serves as both a gateway besides and a remote sensing platform. This paper serves two goals. First, we will review the recent development in architecture, design, and algorithms related to UAVs as a gateway and particularly illustrate its nature in realizing an opportunistic sensing network. Second, recognizing the opportunistic sensing need, we further aim to focus on achieving energy efficiency through developing an active radio frequency (RF)-based wake-up mechanism for aerial–ground data transmission. To prove the effectiveness of energy efficiency, several sensor wake-up solutions are physically implemented and evaluated. The results show that the RF-based wake-up mechanism can potentially save more than 98.4% of the energy that the traditional duty-cycle method would otherwise consume, and 96.8% if an infrared-receiver method is used.

Unmanned Aerial Vehicles for Power Line Inspection: A Cooperative Way in Platforms and Communications

2014 ◽  
Vol 9 (9) ◽  
pp. 687-692 ◽  
Author(s):  
Chuang Deng ◽  
Shengwei Wang ◽  
Zhi Huang ◽  
Zhongfu Tan ◽  
Junyong Liu
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Power Line ◽  
Aerial Vehicles

Vision-aided terrain referenced navigation for unmanned aerial vehicles using ground features

2014 ◽  
Vol 228 (13) ◽  
pp. 2399-2413 ◽  
Author(s):  
Dongjin Lee ◽  
Youngjoo Kim ◽  
Hyochoong Bang
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Navigation System ◽  
Autonomous Navigation ◽  
Low Cost ◽  
Translational Motion ◽  
Inertial Navigation ◽  
Navigation Data ◽  
Terrain Referenced Navigation ◽  
Error Covariance ◽  
Aerial Vehicles

A vision-aided terrain referenced navigation (VATRN) approach is addressed for autonomous navigation of unmanned aerial vehicles (UAVs) under GPS-denied conditions. A typical terrain referenced navigation (TRN) algorithm blends inertial navigation data with measured terrain information to estimate vehicle’s position. In this paper, a low-cost inertial navigation system (INS) for UAVs is supplemented with a monocular vision-aided navigation system and terrain height measurements. A point mass filter based on Bayesian estimation is employed as a TRN algorithm. Homograpies are established to estimate the vehicle’s relative translational motion using ground features with simple assumptions. And the error analysis in homography estimation is explored to estimate the error covariance matrix associated with the visual odometry data. The estimated error covariance is delivered to the TRN algorithm for robust estimation. Furthermore, multiple ground features tracked by image observations are utilized as multiple height measurements to improve the performance of the VATRN algorithm.

scholarly journals UAV-g 2019: Unmanned Aerial Vehicles in Geomatics

Drones ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 74 ◽  
Author(s):  
Nex
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
Autonomous Navigation ◽  
Aerial Vehicles ◽  
Surrounding Environment ◽  
Poster Sessions ◽  
Aerial Vehicle ◽  
The University ◽  
The Impact

Unmanned aerial vehicle in geomatics (UAV-g) is a well-established scientific event dedicated to UAVs in geomatics and remote sensing. In the different editions of the journal, new scientific challenges have increased their synergy with adjacent domains, such as robotics and computer vision, thereby increasing the impact of this conference. The 2019 edition has been hosted by the University of Twente (The Netherlands) and has attracted about 300 participants for the full three-day program. Researchers from 36 different countries (from all continents) have presented 89 accepted papers in 17 oral and 2 poster sessions. The presented papers covered multi-disciplinary topics, such as photogrammetry, natural resources monitoring, autonomous navigation, and deep learning. All these contributions have in common the use of UAV platforms for the innovative acquisition and processing of the acquired data and information extracted from the surrounding environment.

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