Microcomputer Controlled Color Identification Robot Arm

In this twenty first century, there is an increasing need to create artificial arms for different inhuman situations where human interaction is difficult or impossible. The application of robots in solving human needs has enhanced man’s performances, especially in the issue of color blindness in human and inconsistency with color identification. The research work is to design and construct a robot arm that is controlled by a Computer system that can Identify Color. This research work can be achieved by develop a robot arm with four degree of freedom that is downward and upward movement, clockwise and anticlockwise movement. In this system, Modularization Methodology was used which involved the breaking down of large systems into smaller parts (module). Each module was constructed independently to optimize the full function of the entire system. The three primary colors identified were Red, Green and Blue. The identification of a particular color was done by light intensity to frequency converter method. The Arduino Uno provided an interface with the control circuit and computer system via USB cable. The Relay controlled the movement of the robot by sending signal to the DC motors. The TSC3200 programmable color sensor was used to detect and measure the visible range of the colored materials from the work space. The research project successfully carried out the task of identifying the color of object at the preprogrammed work place. This robot can be used as Color sorter and Agricultural machine.

Authors

Institutions

Publication Details

Journal
IOSR Journal of Computer Engineering
Published
2024-12-01
DOI
https://doi.org/10.9790/0661-2606024157
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Microcomputer Controlled Color Identification Robot Arm

Chinagolum Ituma, Ikechukwu Kingsely Nnamani, Nweso Emmanue Nwogbaga, Stephen Chibueze et al.
IOSR Journal of Computer Engineering
Hand Gesture Recognition Systems
article

Microcomputer Controlled Color Identification Robot Arm

Chinagolum Ituma, Ikechukwu Kingsely Nnamani, Nweso Emmanue Nwogbaga, Stephen Chibueze, Benedict Mbanefo Emewu
article en

Abstract

In this twenty first century, there is an increasing need to create artificial arms for different inhuman situations where human interaction is difficult or impossible. The application of robots in solving human needs has enhanced man’s performances, especially in the issue of color blindness in human and inconsistency with color identification. The research work is to design and construct a robot arm that is controlled by a Computer system that can Identify Color. This research work can be achieved by develop a robot arm with four degree of freedom that is downward and upward movement, clockwise and anticlockwise movement. In this system, Modularization Methodology was used which involved the breaking down of large systems into smaller parts (module). Each module was constructed independently to optimize the full function of the entire system. The three primary colors identified were Red, Green and Blue. The identification of a particular color was done by light intensity to frequency converter method. The Arduino Uno provided an interface with the control circuit and computer system via USB cable. The Relay controlled the movement of the robot by sending signal to the DC motors. The TSC3200 programmable color sensor was used to detect and measure the visible range of the colored materials from the work space. The research project successfully carried out the task of identifying the color of object at the preprogrammed work place. This robot can be used as Color sorter and Agricultural machine.

IOSR Journal of Computer EngineeringVol. 26(6)
Ebonyi State University (NG)
Openalex Percentile: Top 74%
Hand Gesture Recognition Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.