Computer vision system for robotic complex based on Arduino and Raspberry Pi

2017 ◽  
Author(s):  
K. A. Petrova
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Raspberry Pi ◽  
Computer Vision System

scholarly journals Application of a computer vision system for recognizing tomato fruits and determining their position relative to the gripper device of the harvesting robot

2021 ◽  
Vol 2091 (1) ◽  
pp. 012063
Author(s):  
A.V. Rybakov ◽  
A.N. Marenkov ◽  
V.A. Kuznetsova ◽  
A.V. Stanishevskaya
Keyword(s):  
Computer Vision ◽  
Inverse Kinematics ◽  
Vision System ◽  
Raspberry Pi ◽  
Computer Vision System ◽  
Tomato Fruits ◽  
Hardware Complex ◽  
Inverse Kinematics Problem ◽  
Plat Form ◽  
Harvesting Robot

Abstract The article presents a method for recognizing tomato fruits covered with foliage, de-termining their centers and boundaries using the OpenCV computer vision library and a hardware complex based on Raspberry Pi 4. The methods for solving the inverse kinematics problem for the five-link robotic manipulator designed by the authors, installed on a mobile plat-form, in order to create a robot for collecting fruits are considered. The simulation of the manipulator movement in the Scilab environment is performed.

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.

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

A CAD-based computer vision system

1998 ◽  
Vol 16 (8) ◽  
pp. 533-539 ◽  
Author(s):  
J.H.M. Byne ◽  
J.A.D.W. Anderson
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Computer Vision System
Sign in / Sign up

Export Citation Format

Share Document