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.

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.

scholarly journals Compressed pseudo-SLAM: pseudorange-integrated compressed simultaneous localisation and mapping for unmanned aerial vehicle navigation

2021 ◽  
pp. 1-13
Author(s):  
Jonghyuk Kim ◽  
Jose Guivant ◽  
Martin L. Sollie ◽  
Torleiv H. Bryne ◽  
Tor Arne Johansen
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Computational Cost ◽  
Satellite System ◽  
Measurement Unit ◽  
Electronic Systems ◽  
Computationally Efficient ◽  
Global Correlation ◽  
Aerial Vehicle ◽  
Correlation Information ◽  
Global Navigation Satellite

Abstract This paper addresses the fusion of the pseudorange/pseudorange rate observations from the global navigation satellite system and the inertial–visual simultaneous localisation and mapping (SLAM) to achieve reliable navigation of unmanned aerial vehicles. This work extends the previous work on a simulation-based study [Kim et al. (2017). Compressed fusion of GNSS and inertial navigation with simultaneous localisation and mapping. IEEE Aerospace and Electronic Systems Magazine, 32(8), 22–36] to a real-flight dataset collected from a fixed-wing unmanned aerial vehicle platform. The dataset consists of measurements from visual landmarks, an inertial measurement unit, and pseudorange and pseudorange rates. We propose a novel all-source navigation filter, termed a compressed pseudo-SLAM, which can seamlessly integrate all available information in a computationally efficient way. In this framework, a local map is dynamically defined around the vehicle, updating the vehicle and local landmark states within the region. A global map includes the rest of the landmarks and is updated at a much lower rate by accumulating (or compressing) the local-to-global correlation information within the filter. It will show that the horizontal navigation error is effectively constrained with one satellite vehicle and one landmark observation. The computational cost will be analysed, demonstrating the efficiency of the method.

Region of interest identification in unmanned aerial vehicle imagery

1997 ◽  
Author(s):  
Jeffrey L. Solka ◽  
David J. Marchette ◽  
George W. Rogers ◽  
Evelyn C. Durling ◽  
John E. Green ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Region Of Interest ◽  
Aerial Vehicle

scholarly journals Design and construction of a prototype of an unmanned aerial vehicle equipped with artificial vision for the search of people

2017 ◽  
Vol 15 (41) ◽  
pp. 9-26
Author(s):  
Andrés Espinal Rojas ◽  
Andrés Arango Espinal ◽  
Luis Ramos ◽  
Jorge Humberto Erazo Aux
Keyword(s):  
Image Processing ◽  
Unmanned Aerial Vehicle ◽  
Low Cost ◽  
Wireless Transmission ◽  
Image Processing Algorithm ◽  
Image Capture ◽  
Aerial View ◽  
Aerial Vehicle ◽  
Recognition Of Objects ◽  
Technological Challenge

This paper describes the development and implementation of a six-pointed Unmanned Aerial Vehicle [UAV] prototype, designed for finding lost people in hard to access areas, using Arduino MultiWii platform. A platform capable of performing a stable flight to identify people through an on-board camera and an image processing algorithm was developed. Although the use of UAV represents a low cost and quick response –in terms of displacement– solution, capable to prevent or reduce the number of deaths of lost people in away places, also represents a technological challenge, since the recognition of objects from an aerial view is difficult, due to the distance of the UAV to the objective, the UAV’s position and its constant movement. The solution proposed implements an aerial device that performs the image capture, wireless transmission and image processing while it is in a controlled and stable flight.

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 Early Fire Detection Based on Aerial 360-Degree Sensors, Deep Convolution Neural Networks and Exploitation of Fire Dynamic Textures

2020 ◽  
Vol 12 (19) ◽  
pp. 3177 ◽  
Author(s):  
Panagiotis Barmpoutis ◽  
Tania Stathaki ◽  
Kosmas Dimitropoulos ◽  
Nikos Grammalidis
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Forest Fires ◽  
Large Scale ◽  
Computational Cost ◽  
Fire Detection ◽  
Adaptive Method ◽  
Field Of View ◽  
Aerial Vehicle ◽  
Remote Sensing Systems ◽  
Early Fire Detection

The environmental challenges the world faces have never been greater or more complex. Global areas that are covered by forests and urban woodlands are threatened by large-scale forest fires that have increased dramatically during the last decades in Europe and worldwide, in terms of both frequency and magnitude. To this end, rapid advances in remote sensing systems including ground-based, unmanned aerial vehicle-based and satellite-based systems have been adopted for effective forest fire surveillance. In this paper, the recently introduced 360-degree sensor cameras are proposed for early fire detection, making it possible to obtain unlimited field of view captures which reduce the number of required sensors and the computational cost and make the systems more efficient. More specifically, once optical 360-degree raw data are obtained using an RGB 360-degree camera mounted on an unmanned aerial vehicle, we convert the equirectangular projection format images to stereographic images. Then, two DeepLab V3+ networks are applied to perform flame and smoke segmentation, respectively. Subsequently, a novel post-validation adaptive method is proposed exploiting the environmental appearance of each test image and reducing the false-positive rates. For evaluating the performance of the proposed system, a dataset, namely the “Fire detection 360-degree dataset”, consisting of 150 unlimited field of view images that contain both synthetic and real fire, was created. Experimental results demonstrate the great potential of the proposed system, which has achieved an F-score fire detection rate equal to 94.6%, hence reducing the number of required sensors. This indicates that the proposed method could significantly contribute to early fire detection.

scholarly journals Using syntactic methods and LSTM to the recognition of objects visual patterns

2019 ◽  
Author(s):  
Gilberto Astolfi ◽  
Vanessa Aparecida de Moares Weber ◽  
Adair Da Silva Oliveira Junior ◽  
Geazy Vilharva Menezes ◽  
Nícolas Alessandro de Souza Belete ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Relevant Information ◽  
Aerial Images ◽  
New Approach ◽  
Visual Patterns ◽  
Aerial Vehicle ◽  
Recognition Of Objects ◽  
F Measure

In this paper, we have designed a new approach to represent and recognize objects visual patterns using syntactic methods. We capture relevant information from an object and associate them with symbols of an alphabet. After that, we derive a string from the object and in put it to LSTM. The idea is to train LSTM with objects visual patterns encapsulated in the strings. We conducted an experiment using soybean crops aerial images captured by an Unmanned Aerial Vehicle (UAV), and we reached an average F-measure of 91%.

Automatic external defibrillator provided by unmanned aerial vehicle (drone) in Greater Paris: A real world-based simulation

Resuscitation ◽  
2021 ◽  
Vol 162 ◽  
pp. 259-265
Author(s):  
Clément Derkenne ◽  
Daniel Jost ◽  
Albane Miron De L’Espinay ◽  
Pascal Corpet ◽  
Benoit Frattini ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Real World ◽  
Automatic External Defibrillator ◽  
Aerial Vehicle ◽  
External Defibrillator
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