scholarly journals Integration of an industrial robot with the systems for image and voice recognition

2013 ◽  
Vol 10 (1) ◽  
pp. 219-230 ◽  
Author(s):  
Jovica Tasevski ◽  
Milutin Nikolic ◽  
Dragisa Miskovic
Keyword(s):  
Computer Vision ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Vision System ◽  
Industrial Robot ◽  
Voice Recognition ◽  
Computer Vision System ◽  
Human Speech ◽  
Position And Orientation ◽  
Asr System

The paper reports a solution for the integration of the industrial robot ABB IRB140 with the system for automatic speech recognition (ASR) and the system for computer vision. The robot has the task to manipulate the objects placed randomly on a pad lying on a table, and the computer vision system has to recognize their characteristics (shape, dimension, color, position, and orientation). The ASR system has a task to recognize human speech and use it as a command to the robot, so the robot can manipulate the objects.

scholarly journals Applying a 6 DoF Robotic Arm and Digital Twin to Automate Fan-Blade Reconditioning for Aerospace Maintenance, Repair, and Overhaul

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4637
Author(s):  
John Oyekan ◽  
Michael Farnsworth ◽  
Windo Hutabarat ◽  
David Miller ◽  
Ashutosh Tiwari
Keyword(s):  
Computer Vision ◽  
Material Removal ◽  
Closed Loop ◽  
Vision System ◽  
Industrial Robot ◽  
Computer Vision System ◽  
Digital Twin ◽  
Fan Blade ◽  
The Uk ◽  
Do So

The UK is home to several major air commercial and transport hubs. As a result, there is a high demand for Maintenance, Repair, and Overhaul (MRO) services to ensure that fleets of aircraft are in airworthy conditions. MRO services currently involve heavy manual labor. This creates bottlenecks, low repeatability, and low productivity. Presented in this paper is an investigation to create an automation cell for the fan-blade reconditioning component of MRO. The design and prototype of the automation cell is presented. Furthermore, a digital twin of the grinding process is developed and used as a tool to explore the required grinding force parameters needed to effectively remove surface material. An integration of a 6-DoF industrial robot with an end-effector grinder and a computer vision system was undertaken. The computer vision system was used for the digitization of the fan-blade surface as well as tracking and guidance of material removal. Our findings reveal that our proposed system can perform material removal, track the state of the fan blade during the reconditioning process and do so within a closed-loop automated robotic work cell.

Colour assessment of milk and milk products using computer vision system and colorimeter

2021 ◽  
pp. 105084
Author(s):  
Bojana Milovanovic ◽  
Ilija Djekic ◽  
Jelena Miocinovic ◽  
Bartosz G. Solowiej ◽  
Jose M. Lorenzo ◽  
...  
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Computer Vision System ◽  
Milk Products

scholarly journals An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 343
Author(s):  
Kim Bjerge ◽  
Jakob Bonde Nielsen ◽  
Martin Videbæk Sepstrup ◽  
Flemming Helsing-Nielsen ◽  
Toke Thomas Høye
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Vision System ◽  
Low Cost ◽  
Light Trap ◽  
Automatic Monitoring ◽  
Light Sources ◽  
Monitoring Methods ◽  
Computer Vision System ◽  
Substantial Investment

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

scholarly journals Task Planner for Robotic Disassembly of Electric Vehicle Battery Pack

Metals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 387
Author(s):  
Martin Choux ◽  
Eduard Marti Bigorra ◽  
Ilya Tyapin
Keyword(s):  
Computer Vision ◽  
Electric Vehicles ◽  
Measurement Errors ◽  
Vision System ◽  
Lithium Ion ◽  
Battery Pack ◽  
Computer Vision System ◽  
Electric Vehicle Battery ◽  
Robotic Disassembly ◽  
Total Average

The rapidly growing deployment of Electric Vehicles (EV) put strong demands on the development of Lithium-Ion Batteries (LIBs) but also into its dismantling process, a necessary step for circular economy. The aim of this study is therefore to develop an autonomous task planner for the dismantling of EV Lithium-Ion Battery pack to a module level through the design and implementation of a computer vision system. This research contributes to moving closer towards fully automated EV battery robotic dismantling, an inevitable step for a sustainable world transition to an electric economy. For the proposed task planner the main functions consist in identifying LIB components and their locations, in creating a feasible dismantling plan, and lastly in moving the robot to the detected dismantling positions. Results show that the proposed method has measurement errors lower than 5 mm. In addition, the system is able to perform all the steps in the order and with a total average time of 34 s. The computer vision, robotics and battery disassembly have been successfully unified, resulting in a designed and tested task planner well suited for product with large variations and uncertainties.

Real-time Wearable Computer Vision System for Improved Museum Experience

2016 ◽  
Author(s):  
Giovanni Taverriti ◽  
Stefano Lombini ◽  
Lorenzo Seidenari ◽  
Marco Bertini ◽  
Alberto Del Bimbo
Keyword(s):  
Computer Vision ◽  
Real Time ◽  
Vision System ◽  
Wearable Computer ◽  
Computer Vision System ◽  
Museum Experience

scholarly journals Evaluating the diameter of eyedropper tips using a computer vision system

2019 ◽  
Vol 82 (1) ◽  
Author(s):  
Edicley Vander Machado ◽  
Priscila Cardoso Cristovam ◽  
Denise de Freitas ◽  
José Álvaro Pereira Gomes ◽  
Vagner Rogério dos Santos
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Computer Vision System
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