scholarly journals Aerial Grasping and Transport Using an Unmanned Aircraft (UA) Equipped with an Industrial Suction Gripper

2022 ◽  
pp. 89-99
Author(s):  
Markus Lieret ◽  
Benedikt Kreis ◽  
Christian Hofmann ◽  
Maximilian Zwingel ◽  
Jörg Franke
Keyword(s):  
Pose Estimation ◽  
System Performance ◽  
Research Area ◽  
Unmanned Aircraft ◽  
Use Case ◽  
Precise Localization ◽  
Relevant Research ◽  
Reliable Identification ◽  
Object Interaction ◽  
Cargo Transport

AbstractDuetotheavailabilityof highly efficient unmanned aircraft (UA) and the advancement of the necessary technologies, the use of UA for object manipulation and cargo transport is becoming a more and more relevant research area. A reliable identification and localization of cargo and interaction objects as well as maintaining the required flight precision are essential to guarantee a successful object handling. Within this paper we demonstrate the successful application of an autonomous UA equipped with a lightweight suction gripper for object interaction. We discuss the approach used for precise localization as well as the identification and pose estimation of individual gripping objects. Concluding, the overall system performance is evaluated within an industrial-oriented use case.

Multi-Level Fusion Net for hand pose estimation in hand-object interaction

2021 ◽  
Vol 94 ◽  
pp. 116196
Author(s):  
Xiang-Bo Lin ◽  
Yi-Dan Zhou ◽  
Kuo Du ◽  
Yi Sun ◽  
Xiao-Hong Ma ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Hand Pose Estimation ◽  
Object Interaction ◽  
Multi Level ◽  
Level Fusion ◽  
Hand Pose

Addressing pose estimation issues for machine vision based UAV autonomous serial refuelling

2007 ◽  
Vol 111 (1120) ◽  
pp. 389-396 ◽  
Author(s):  
G. Campa ◽  
M. R. Napolitano ◽  
M. Perhinschi ◽  
M. L. Fravolini ◽  
L. Pollini ◽  
...  
Keyword(s):  
Machine Vision ◽  
Pose Estimation ◽  
Estimation Algorithm ◽  
Computational Effort ◽  
Unmanned Aircraft ◽  
Light Sources ◽  
Estimation Algorithms ◽  
Trade Offs ◽  
Labeling Algorithms ◽  
Selection Of

Abstract This paper describes the results of an effort on the analysis of the performance of specific ‘pose estimation’ algorithms within a Machine Vision-based approach for the problem of aerial refuelling for unmanned aerial vehicles. The approach assumes the availability of a camera on the unmanned aircraft for acquiring images of the refuelling tanker; also, it assumes that a number of active or passive light sources – the ‘markers’ – are installed at specific known locations on the tanker. A sequence of machine vision algorithms on the on-board computer of the unmanned aircraft is tasked with the processing of the images of the tanker. Specifically, detection and labeling algorithms are used to detect and identify the markers and a ‘pose estimation’ algorithm is used to estimate the relative position and orientation between the two aircraft. Detailed closed-loop simulation studies have been performed to compare the performance of two ‘pose estimation’ algorithms within a simulation environment that was specifically developed for the study of aerial refuelling problems. Special emphasis is placed on the analysis of the required computational effort as well as on the accuracy and the error propagation characteristics of the two methods. The general trade offs involved in the selection of the pose estimation algorithm are discussed. Finally, simulation results are presented and analysed.

scholarly journals Deep Learning based Human Action Recognition

2021 ◽  
Vol 40 ◽  
pp. 03014
Author(s):  
Ritik Pandey ◽  
Yadnesh Chikhale ◽  
Ritik Verma ◽  
Deepali Patil
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Research Area ◽  
Video Clips ◽  
Object Interaction ◽  
Important Research Area ◽  
Multiple Frames

Human action recognition has become an important research area in the fields of computer vision, image processing, and human-machine or human-object interaction due to its large number of real time applications. Action recognition is the identification of different actions from video clips (an arrangement of 2D frames) where the action may be performed in the video. This is a general construction of image classification tasks to multiple frames and then collecting the predictions from each frame. Different approaches are proposed in literature to improve the accuracy in recognition. In this paper we proposed a deep learning based model for Recognition and the main focus is on the CNN model for image classification. The action videos are converted into frames and pre-processed before sending to our model for recognizing different actions accurately..

AvDR-Based Wireless Secure Key Generation with Colored Noise for IoT

2019 ◽  
Vol 19 (01) ◽  
pp. 2050013
Author(s):  
Ankit Soni ◽  
Raksha Upadhyay ◽  
Abhay Kumar
Keyword(s):  
Dimensionality Reduction ◽  
System Performance ◽  
Colored Noise ◽  
Additive White Gaussian Noise ◽  
Principal Component ◽  
Research Area ◽  
Wireless Channel ◽  
Resource Constraint ◽  
Key Generation ◽  
Correlated Components

Physical layer key generation exploiting inherent channel randomness is an open research area in securing the networks with resource constraint nodes; therefore reduction of numerical computation is desirable to save battery power. However, the correlated components due to colored noise also affect the system performance. In this work, we consider the correlated colored noise components along with the additive white Gaussian noise (AWGN) in the wireless channel and analyze the effect of these correlated components on the system performance. We further propose a hybrid averaging and dimensionality reduction (AvDR), based received signal strength (RSS) preprocessing which is the combination of moving window averaging (Av) and principal component analysis (PCA) as dimensionality reduction technique (DR) to improve the system performance. Further, the system performance was evaluated by numerical simulations, and it is observed that the same improvement in system performance is achieved by generating keys from a fewer number of points selected after PCA as compared to processing all the points. Picking a few of the points in the data sequence instead of all reduces the total number of numerical calculations and saves system power, which is the primary requirement of resource constraint networks like the IoT.

Domain Specific Custom Search for Quicker Information Retrieval

2013 ◽  
Vol 3 (3) ◽  
pp. 26-39
Author(s):  
Tushar Kanti Saha ◽  
A. B. M. Shawkat Ali
Keyword(s):  
Research Area ◽  
Google Scholar ◽  
Research Articles ◽  
Search Technique ◽  
Relevant Research ◽  
Domain Specific ◽  
First Time ◽  
Specific Search ◽  
Specific Country ◽  
Quick Search

Recently researchers are using Google scholar widely to find out the research articles and the relevant experts in their domain. But it is unable to find out all experts in a relevant research area from a specific country by a quick search. Basically the custom search technique is not available in the current Google scholar’s setup. The authors have combined custom search with domain-specific search and named as domain specific custom search in this research. First time this research introduces a domain specific custom search technique using new search methodology called n-paged-m-items partial crawling algorithm. This algorithm is a real-time faster crawling algorithm due to the partial crawling technique. It does not store anything in the database, which can be shown later on to the user. The proposed algorithm is implemented on a new domain scholar.google.com to find out the scholars or experts quickly. Finally the authors observe the better performance of the proposed algorithm comparing with Google scholar.

scholarly journals MANTRA: An Effective System Based on Augmented Reality and Infrared Thermography for Industrial Maintenance

2021 ◽  
Vol 11 (1) ◽  
pp. 385
Author(s):  
Mario Ortega ◽  
Eugenio Ivorra ◽  
Alejandro Juan ◽  
Pablo Venegas ◽  
Jorge Martínez ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Infrared Thermography ◽  
Pose Estimation ◽  
Detection Method ◽  
Estimation Method ◽  
Use Case ◽  
Efficient Manner ◽  
Pose Tracking ◽  
Effective System ◽  
Industrial Maintenance

In recent years, the benefits of both Augmented Reality (AR) technology and infrared thermography (IRT) have been demonstrated in the industrial maintenance sector, allowing maintenance operations to be carried out in a safer, faster, and more efficient manner. However, there still exists no solution that optimally combines both technologies. In this work, we propose a new AR system—MANTRA—with specific application to industrial maintenance. The system can automatically align virtual information and temperature on any 3D object, in real time. This is achieved through the joint use of an RGB-D sensor and an IRT camera, leading to high accuracy and robustness. To achieve this objective, a pose estimation method that combines a deep-learning-based object detection method, YOLOV4, together with the template-based LINEMOD pose estimation method, as well as a model-based 6DOF pose tracking technique, was developed. The MANTRA system is validated both quantitatively and qualitatively through a real use-case, demonstrating the effectiveness of the system compared to traditional methods and those using only AR.

scholarly journals Inverted Hierarchies on the Shop Floor: The Organisational Layer of Workarounds for Collaboration in the Metal Industry

2021 ◽  
Author(s):  
Frauke Mörike
Keyword(s):  
Collaborative Work ◽  
Research Area ◽  
Metal Industry ◽  
Shop Floor ◽  
Work Processes ◽  
Relevant Research ◽  
Organisational Context ◽  
Information Power ◽  
A Company ◽  
Insight Into

AbstractWorkarounds, or practices that deviate from the official pathway to a target, are frequent phenomena in the organisational context. With respect to collaboration, they highlight an area of mismatch between normative versus lived work practices, and therefore depict a relevant research area deeply rooted in computer supported cooperative work (CSCW). Building on the theory of hierarchical opposition by Louis Dumont and empirical data collected through ethnographic research at a company classified as a small- and medium-sized enterprise (SME) in the German metal industry, this paper addresses the emergence of workarounds in collaborative work processes by setting them into the wider organisational context. The organisational layer of analysis reveals that workarounds emerge to cater for inversed information power relations and information asymmetries in the shop floor setting, which require communication to flow against the hierarchical slope between planning and execution functions. By applying an organisational lens to the concept of workarounds, this paper contributes a novel empirical analysis that confirms the value of workarounds as a source of insight into collaborative practices.

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