Development of Wire Suspended Robot System (ARANEUS 2.0) for Bridge Inspection

10.29007/zw9k ◽  
2020 ◽  
Author(s):  
Kazuhide Nakata ◽  
Kazuki Umemoto ◽  
Kenji Kaneko ◽  
Ryusuke Fujisawa
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Concrete Surface ◽  
Wire Length ◽  
High Definition ◽  
Bridge Inspection ◽  
Robot System ◽  
Inspection Method ◽  
Aerial Vehicle ◽  
The Wire

This study addresses the development of a robot for inspection of old bridges. By suspending the robot with a wire and controlling the wire length, the movement of the robot is realized. The robot mounts a high-definition camera and aims to detect cracks on the concrete surface of the bridge using this camera. An inspection method using an unmanned aerial vehicle (UAV) has been proposed. Compared to the method using an unmanned aerial vehicle, the wire suspended robot system has the advantage of insensitivity to wind and ability to carry heavy equipments, this makes it possible to install a high-definition camera and a cleaning function to find cracks that are difficult to detect due to dirt.

Stable camera position control of unmanned aerial vehicle with three‐degree‐of‐freedom manipulator for visual test of bridge inspection

2019 ◽  
Vol 36 (7) ◽  
pp. 1212-1221
Author(s):  
Takahiro Ikeda ◽  
Satoshi Minamiyama ◽  
Shogo Yasui ◽  
Kenichi Ohara ◽  
Akihiko Ichikawa ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Position Control ◽  
Degree Of Freedom ◽  
Visual Test ◽  
Bridge Inspection ◽  
Aerial Vehicle ◽  
Camera Position ◽  
Three Degree Of Freedom

scholarly journals Utilization of Unmanned Aerial Vehicle, Artificial Intelligence, and Remote Measurement Technology for Bridge Inspections

2020 ◽  
Vol 32 (6) ◽  
pp. 1244-1258
Author(s):  
Pang-jo Chun ◽  
Ji Dang ◽  
Shunsuke Hamasaki ◽  
Ryosuke Yajima ◽  
Toshihiro Kameda ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Unmanned Aerial Vehicle ◽  
Measurement Technology ◽  
Remote Measurement ◽  
Bridge Inspection ◽  
Current State ◽  
Bridge Collapse ◽  
Bridge Inspections ◽  
Aerial Vehicle

In recent years, aging of bridges has become a growing concern, and the danger of bridge collapse is increasing. To appropriately maintain bridges, it is necessary to perform inspections to accurately understand their current state. Until now, bridge inspections have involved a visual inspection in which inspection personnel come close to the bridges to perform inspection and hammering tests to investigate abnormal noises by hammering the bridges with an inspection hammer. Meanwhile, as there are a large number of bridges (for example, 730,000 bridges in Japan), and many of these are constructed at elevated spots; the issue is that the visual inspections are laborious and require huge cost. Another issue is the wide disparity in the quality of visual inspections due to the experience, knowledge, and competence of inspectors. Accordingly, the authors are trying to resolve or ameliorate these issues using unmanned aerial vehicle (UAV) technology, artificial intelligence (AI) technology, and telecommunications technology. This is discussed first in this paper. Next, the authors discuss the future prospects of bridge inspection using robot technology such as a 3-D model of bridges. The goal of this paper is to show the areas in which deployment of the UAV, robots, telecommunications, and AI is beneficial and the requirements of these technologies.

scholarly journals Application of Crack Identification Techniques for an Aging Concrete Bridge Inspection Using an Unmanned Aerial Vehicle

Sensors ◽  
2018 ◽  
Vol 18 (6) ◽  
pp. 1881 ◽  
Author(s):  
In-Ho Kim ◽  
Haemin Jeon ◽  
Seung-Chan Baek ◽  
Won-Hwa Hong ◽  
Hyung-Jo Jung
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Crack Identification ◽  
Concrete Bridge ◽  
Bridge Inspection ◽  
Aerial Vehicle ◽  
Identification Techniques

scholarly journals Unmanned Aerial Patrol Technology Based on Tracking Algorithm of Target Tracking

2018 ◽  
Vol 14 (11) ◽  
pp. 160
Author(s):  
Yao Yao ◽  
Qing-le Quan ◽  
Hong-hui Zhang ◽  
Qiong Li
Keyword(s):  
Kalman Filter ◽  
Machine Vision ◽  
Target Tracking ◽  
Unmanned Aerial Vehicle ◽  
Transmission Lines ◽  
Region Of Interest ◽  
Power Line ◽  
Tracking Algorithm ◽  
Inspection Method ◽  
Aerial Vehicle

<p class="0abstract"><span lang="EN-US">In order to study the power patrol technology of unmanned aerial vehicle, the tracking algorithm was applied. The automatic patrolling of power lines was discussed in terms of algorithms. An unmanned aerial vehicle transmission line inspection method based on machine vision was proposed. The image and video of the unmanned aerial vehicle inspection of the power line had a complex background. By Wiener filtering de-noising and probability density functions, the image clarity was improved. According to the existing tracking techniques and algorithms, a Camshaft target tracking algorithm based on lossless Kalman filter was proposed. The method of non-destructive Kalman filter was adopted to predict the region of interest of power line identification. Using the Camshaft algorithm, the prediction of the window was searched and the size of the window was adjusted. Transmission lines were tracked in real time. The results showed that the restoration effect of the algorithm was obvious. The clarity of the image was improved. It prepared for the extraction and tracking of the future transmission lines. Therefore, the proposed method provides a feasible way for the UAV power line inspection technology based on machine vision.</span></p>

scholarly journals Aerial Manipulator Control Method Based on Generalized Jacobian

2021 ◽  
Vol 33 (2) ◽  
pp. 231-241
Author(s):  
Takahiro Ikeda ◽  
Kenichi Ohara ◽  
Akihiko Ichikawa ◽  
Satoshi Ashizawa ◽  
Takeo Oomichi ◽  
...  
Keyword(s):  
Coordinate System ◽  
Unmanned Aerial Vehicle ◽  
Control Method ◽  
The Body ◽  
Degree Of Freedom ◽  
Bridge Inspection ◽  
Generalized Jacobian ◽  
Aerial Manipulator ◽  
Aerial Vehicle ◽  
Manipulator Control

This paper describes a control method for an aerial manipulator on an unmanned aerial vehicle (UAV) by using a generalized Jacobian (GJ). Our task is to realize visual check of bridge inspection by employing a UAV with a multi-degree-of-freedom (DoF) manipulator on its top. The manipulator is controlled by using the GJ. Subsequently, by comparing the aerial manipulator control with a conventional Jacobian experimentally, we discovered that the accuracy of the control improved by applying the GJ. The manipulator has three DoFs in the X-Z plane of the UAV coordinate system. The experiment shows that the manipulator controlled with the GJ can compensate for the pose error of the body by 54.5% and 47.7% in the X- and Z-axes, respectively.

Geometric Relation-Based Cognitive Sharing for Flying and Ground Mobile Robot Cooperation

2016 ◽  
Vol 20 (6) ◽  
pp. 919-927
Author(s):  
Yifeng Cai ◽  
◽  
Kosuke Sekiyama
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Real World ◽  
Computational Cost ◽  
Region Of Interest ◽  
Robot System ◽  
Unstructured Environment ◽  
Aerial Vehicle ◽  
Geometric Relation ◽  
Recognition Of Objects ◽  
Robot Cooperation

Cognitive sharing of objects is fundamental in a heterogeneous robot system composed of a Unmanned Aerial Vehicle and a ground robot. Since the viewpoint of a UAV is greatly different from a ground robot, they may have different perceptions about the same objects. That makes it difficult to realize cognitive sharing. In this paper, we proposed a cognitive sharing method which is based on Geometric Relation-based Triangle Representations. The method is able to make a UAV and a ground robot identify the same object from similar objects without sharing appearance information in unstructured environment. To copy with the problem of increasing computational cost for the recognition of objects in the Region of Interest, entropy evaluation is employed to evaluate and select unique representations. We illustrated the proposed method with robots in real world.

Design of a new low-cost unmanned aerial vehicle and vision-based concrete crack inspection method

2020 ◽  
Vol 19 (6) ◽  
pp. 1871-1883 ◽  
Author(s):  
Bin Lei ◽  
Yali Ren ◽  
Ning Wang ◽  
Linsheng Huo ◽  
Gangbing Song
Keyword(s):  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Concrete Structure ◽  
Low Cost ◽  
Central Point ◽  
Point Method ◽  
Great Promise ◽  
Support Vector ◽  
Inspection Method ◽  
Aerial Vehicle

With the explosive development of the computer vision technology, more and more vision-based inspection methods enabled by unmanned aerial vehicle technologies have been researched on the crack inspection of the sundry concrete structures. However, because of the limitation of the low-cost unmanned aerial vehicle hardware, whose cost is around US$500, most of the vision-based methods are difficult to be implemented on the low-cost unmanned aerial vehicle for real-time crack inspection. To address this challenge, in this article, a new computationally efficient vision-based crack inspection method is designed and successfully implemented on a low-cost unmanned aerial vehicle. Furthermore, to reduce the acquired data samples, a new algorithm entitled crack central point method is designed to extract the effective information from the pre-processed images. The proposed vision-based crack detection method includes the following three major components: (1) the image pre-processing algorithm, (2) crack central point method, and (3) the support vector machine model–based classifier. To demonstrate the effectiveness of the new inspection method, a concrete structure inspection experiment is implemented. The experimental results indicate that this new method is able to accurately and rapidly inspect the cracks of concrete structure in real time. This new vision-based crack inspection method shows great promise for the practical application.

scholarly journals Automated Counting of Rice Panicle by Applying Deep Learning Model to Images from Unmanned Aerial Vehicle Platform

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3106 ◽  
Author(s):  
Chengquan Zhou ◽  
Hongbao Ye ◽  
Jun Hu ◽  
Xiaoyan Shi ◽  
Shan Hua ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Field Experiments ◽  
Disease Diagnosis ◽  
Global Scale ◽  
High Definition ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Detection And Counting ◽  
Aerial Vehicle

The number of panicles per unit area is a common indicator of rice yield and is of great significance to yield estimation, breeding, and phenotype analysis. Traditional counting methods have various drawbacks, such as long delay times and high subjectivity, and they are easily perturbed by noise. To improve the accuracy of rice detection and counting in the field, we developed and implemented a panicle detection and counting system that is based on improved region-based fully convolutional networks, and we use the system to automate rice-phenotype measurements. The field experiments were conducted in target areas to train and test the system and used a rotor light unmanned aerial vehicle equipped with a high-definition RGB camera to collect images. The trained model achieved a precision of 0.868 on a held-out test set, which demonstrates the feasibility of this approach. The algorithm can deal with the irregular edge of the rice panicle, the significantly different appearance between the different varieties and growing periods, the interference due to color overlapping between panicle and leaves, and the variations in illumination intensity and shading effects in the field. The result is more accurate and efficient recognition of rice-panicles, which facilitates rice breeding. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a global scale.

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