scholarly journals Implementation of an Artificial Vision System for Welding in the Retrofitting Process of a Robotic Arm Industrial

2020 ◽  
Author(s):  
Yomin Estiven Jaramillo Munera ◽  
Jhon Edison Goez Mora ◽  
Juan Camilo Londoño Lopera ◽  
Edgar Mario Rico Mesa
Keyword(s):  
Vision System ◽  
Artificial Vision ◽  
Robotic Arm

scholarly journals Design and Construction of a Cost-Effective Didactic Robotic Arm for Playing Chess, Using an Artificial Vision System

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1154 ◽  
Author(s):  
Cristian del Toro ◽  
Carlos Robles-Algarín ◽  
Omar Rodríguez-Álvarez
Keyword(s):  
Visual Servoing ◽  
Mechanical Design ◽  
Vision System ◽  
Cost Effective ◽  
Artificial Vision ◽  
Robotic Arm ◽  
Chess Game ◽  
Human Opponent ◽  
The University ◽  
Design And Construction

This paper presents the design and construction of a robotic arm that plays chess against a human opponent, based on an artificial vision system. The mechanical design was an adaptation of the robotic arm proposed by the rapid prototyping laboratory FabLab RUC (Fabrication Laboratory of the University of Roskilde). Using the software Solidworks, a gripper with 4 joints was designed. An artificial vision system was developed for detecting the corners of the squares on a chessboard and performing image segmentation. Then, an image recognition model was trained using convolutional neural networks to detect the movements of pieces on the board. An image-based visual servoing system was designed using the Kanade–Lucas–Tomasi method, in order to locate the manipulator. Additionally, an Arduino development board was programmed to control and receive information from the robotic arm using Gcode commands. Results show that with the Stockfish chess game engine, the system is able to make game decisions and manipulate the pieces on the board. In this way, it was possible to implement a didactic robotic arm as a relevant application in data processing and decision-making for programmable automatons.

A Two-Channel Artificial Vision System for Motion Analysis

2003 ◽  
Vol 43 (9) ◽  
pp. 1271-1279
Author(s):  
Alexis Quesada-Arencibia ◽  
Roberto Moreno-Díaz ◽  
Miguel Aleman-Flores
Keyword(s):  
Motion Analysis ◽  
Vision System ◽  
Artificial Vision

scholarly journals Ripening Stages Control of Cherry Tomato Coated with Aloe Vera Gel using Artificial Vision System

2020 ◽  
Vol 17 (105) ◽  
pp. 135-149
Author(s):  
Ali Ganjloo ◽  
Mohsen Zandi ◽  
Mandana Bimakr ◽  
Samaneh Monajem ◽  
◽  
...  
Keyword(s):  
Vision System ◽  
Aloe Vera ◽  
Artificial Vision ◽  
Cherry Tomato ◽  
Ripening Stages ◽  
Aloe Vera Gel

New Redundant Manipulator Robot with Six Degrees of Freedom Controlled with Visual Feedback

2017 ◽  
pp. 231-255
Author(s):  
Claudio Urrea ◽  
Héctor Araya
Keyword(s):  
Visual Feedback ◽  
Degrees Of Freedom ◽  
Position Control ◽  
Vision System ◽  
Artificial Vision ◽  
Correct Operation ◽  
Six Degrees Of Freedom ◽  
Design And Implementation ◽  
Implementation Stages ◽  
Manipulator Robot

The design and implementation stages of a redundant robotized manipulator with six Degrees Of Freedom (DOF), controlled with visual feedback by means of computational software, is presented. The various disciplines involved in the design and implementation of the manipulator robot are highlighted in their electric as well as mechanical aspects. Then, the kinematics equations that govern the position and orientation of each link of the manipulator robot are determined. The programming of an artificial vision system and of an interface that control the manipulator robot is designed and implemented. Likewise, the type of position control applied to each joint is explained, making a distinction according to the assigned task. Finally, functional mechanical and electric tests to validate the correct operation of each of the systems of the manipulator robot and the whole robotized system are carried out.

ROBOTIC ARM WITH BIoT MACHINE LEARNING SYSTEM

Fractals ◽  
2020 ◽  
Vol 28 (04) ◽  
pp. 2050088
Author(s):  
R. CARREÑO AGUILERA ◽  
F. AGUILAR ACEVEDO ◽  
M. PATIÑO ORTIZ ◽  
J. PATIÑO ORTIZ
Keyword(s):  
Machine Learning ◽  
Expert System ◽  
Internet Of Things ◽  
Reference Points ◽  
Learning System ◽  
Artificial Vision ◽  
Robotic Arm ◽  
The Internet ◽  
Limiting Factor ◽  
Flat Surfaces

In this work, we present a robotic arm assisted by a visual system to decide whether an object with different colors, parallel flat surfaces and other types of surfaces would be subject to be manipulated without a drop risk. This robotic arm is assisted with sensors such as temperature, humidity, artificial vision, etc. and monitored with a Blockchain Internet of Things (BIoT) expert system assistance, which is shared and accessed by the internet by the users. A prototype for industrial purpose is launched to start providing data for training the expert system, achieving in this way an expert system with machine learning. The variations derived from the identification of the reference points and the characteristics of the robotic arm are a limiting factor of the system, however, it was possible to correctly locate the robotic arm in the workspace to take the object and manipulate it using machine learning based on a BIoT expert system.

scholarly journals A Neural Network Based Artificial Vision System for Licence Plate Recognition

1997 ◽  
Vol 08 (01) ◽  
pp. 113-126 ◽  
Author(s):  
Sorin Draghici
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Vision System ◽  
High Reliability ◽  
Artificial Vision ◽  
Training Algorithms ◽  
Real World Data ◽  
Registration Number ◽  
Plate Location ◽  
High Level

This paper presents a neural network based artificial vision system able to analyze the image of a car given by a camera, locate the registration plate and recognize the registration number of the car. The paper describes in detail various practical problems encountered in implementing this particular application and the solution used to solve them. The main features of the system presented are: controlled stability-plasticity behavior, controlled reliability threshold, both off-line and on-line learning, self assessment of the output reliability and high reliability based on high level multiple feedback. The system has been designed using a modular approach. Sub-modules can be upgraded and/or substituted independently, thus making the system potentially suitable in a large variety of vision applications. The OCR engine was designed as an interchangeable plug-in module. This allows the user to choose an OCR engine which is suited to the particular application and to upgrade it easily in the future. At present, there are several versions of this OCR engine. One of them is based on a fully connected feedforward artificial neural network with sigmoidal activation functions. This network can be trained with various training algorithms such as error backpropagation. An alternative OCR engine is based on the constraint based decomposition (CBD) training architecture. The system has showed the following performances (on average) on real-world data: successful plate location and segmentation about 99%, successful character recognition about 98% and successful recognition of complete registration plates about 80%

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