Next Article in Journal
An Attempt to Model the Surface Pressures of Apples Using the Finite Element Method
Next Article in Special Issue
Smart Manufacturing Technology
Previous Article in Journal
In-Cylinder Oxygen Concentration Estimation Based on Virtual Measurement and Data Fusion Algorithm for Turbocharged Diesel Engines
Previous Article in Special Issue
Research on Temperature Compensation Method in Crankshaft Online Measurement System
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 11
Issue 16
10.3390/app11167593
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

HSV Color-Space-Based Automated Object Localization for Robot Grasping without Prior Knowledge

by
Hyun-Chul Kang
1,
Hyo-Nyoung Han
1,
Hee-Chul Bae
1,
Min-Gi Kim
1,
Ji-Yeon Son
1 and
Young-Kuk Kim
2,*
1
Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
2
Department of Computer Engineering, Chungnam National University, Daejeon 34134, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2021, 11(16), 7593; https://doi.org/10.3390/app11167593
Submission received: 30 June 2021 / Revised: 3 August 2021 / Accepted: 16 August 2021 / Published: 18 August 2021
(This article belongs to the Special Issue Smart Manufacturing Technology)
Browse Figures
Versions Notes

Abstract

We propose a simple and robust HSV color-space-based algorithm that can automatically extract object position information without human intervention or prior knowledge. In manufacturing sites with high variability, it is difficult to recognize products through robot machine vision, especially in terms of extracting object information accurately, owing to various environmental factors such as the noise around objects, shadows, light reflections, and illumination interferences. The proposed algorithm, which does not require users to reset the HSV color threshold value whenever a product is changed, uses ROI referencing method to solve this problem. The algorithm automatically identifies the object’s location by using the HSV color-space-based ROI random sampling, ROI similarity comparison, and ROI merging. The proposed system utilizes an IoT device with several modules for the detection, analysis, control, and management of object data. The experimental results show that the proposed algorithm is very useful for industrial automation applications under complex and highly variable manufacturing environments.
Keywords: object localization; HSV color space; teaching-less robot; smart factory object localization; HSV color space; teaching-less robot; smart factory

Share and Cite

MDPI and ACS Style

Kang, H.-C.; Han, H.-N.; Bae, H.-C.; Kim, M.-G.; Son, J.-Y.; Kim, Y.-K. HSV Color-Space-Based Automated Object Localization for Robot Grasping without Prior Knowledge. Appl. Sci. 2021, 11, 7593. https://doi.org/10.3390/app11167593

AMA Style

Kang H-C, Han H-N, Bae H-C, Kim M-G, Son J-Y, Kim Y-K. HSV Color-Space-Based Automated Object Localization for Robot Grasping without Prior Knowledge. Applied Sciences. 2021; 11(16):7593. https://doi.org/10.3390/app11167593

Chicago/Turabian Style

Kang, Hyun-Chul, Hyo-Nyoung Han, Hee-Chul Bae, Min-Gi Kim, Ji-Yeon Son, and Young-Kuk Kim. 2021. "HSV Color-Space-Based Automated Object Localization for Robot Grasping without Prior Knowledge" Applied Sciences 11, no. 16: 7593. https://doi.org/10.3390/app11167593

APA Style

Kang, H.-C., Han, H.-N., Bae, H.-C., Kim, M.-G., Son, J.-Y., & Kim, Y.-K. (2021). HSV Color-Space-Based Automated Object Localization for Robot Grasping without Prior Knowledge. Applied Sciences, 11(16), 7593. https://doi.org/10.3390/app11167593

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop