Smart Robot Arm Motion Using Computer Vision

Iscimen, Bilal; Atasoy, Huseyin; Kutlu, Yakup; Yıldırım, Serdar; Yıldırım, Esen

doi:10.5755/j01.eee.21.6.13749

Cited by 10 publications

(16 citation statements)

References 3 publications

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“…Iscimen et al presented a smart robotic arm motion using computer vision to identify objects from images automatically and perform the given tasks. (9) Of special interest, an artificial neural network was used for object recognition, and an overall recognition accuracy of 98.30% was achieved. Di Fulvio et al proposed a stereoscopic vision system for conducting dimensional measurements in an industrial robotics application.…”

Section: Introductionmentioning

confidence: 99%

Development of a Color Object Classification and Measurement System Using Machine Vision

Ngo¹,

Porter²,

Hsu³

2019

Sensors and Materials

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Machine-vision-based reading and sorting devices have been used to measure and classify items. Here, we extend their application to the sorting and assembling of items identified by their geometry and color. In this study, we developed an improved machine vision system that is capable of discerning and categorizing items of distinct geometries and colors and utilizes a computer-controlled robotic system to manipulate and segregate these items. Hence, a machine vision system for an automatic classification process while operating a robotic arm is hereby developed. To obtain positioning information, the proposed system uses cameras that were mounted above the working platform to acquire images. Perspective and quadratic transformations were used to transform the image coordinates of the calibration system to the world coordinates by using a calibration procedure. By these methods, the proposed system can ascertain the two-and three-dimensional coordinates of the objects and automatically perform classification and assembly operations using the data collected from the visual recognition system.

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Section: Introductionmentioning

confidence: 99%

Development of a Color Object Classification and Measurement System Using Machine Vision

Ngo¹,

Porter²,

Hsu³

2019

Sensors and Materials

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“…In most applications, computers are preprogramed to perform a specific task that makes use of computer vision. Controlling processes, navigation, detecting events or existence of any objects, modelling environments or objects are some examples of computer vision based applications (Forsyth and Ponce, 2002;Manzoor et al, 2014;Atasoy et al, 2015;.…”

Section: Introductionmentioning

confidence: 99%

Classification of Serranidae Species Using Color Based Statistical Features

Iscimen¹,

Kutlu²,

Turan³

2017

Natural and Engineering Sciences

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In this study 6 species of Serranidae family (Epinephelus aeneus, Epinephelus caninus, Epinephelus costae, Epinephelus marginatus, Hyporthodus haifensis, Mycteroperca rubra) were classified by using a color based feature extraction method. A database which consists of 112 fish images was used in this study. In each image, a fish was located on a white background floor with the same position and the images were taken from different distances. A combination of manual processes and automatic algorithms were applied on images until obtaining colored fish sample images with a black background. Since the presented color based feature extraction method avoids including background, these images were processed by using an automatic algorithm in order to obtain a solid texture image from the fish and extract features. The obtained solid texture image was in HSV color space and used due to extract species-specific information from the fish samples. Each of the hue, saturation and value components of the HSV color space was used separately in order to extract 7 statistical features. Hence, totally 21 features were extracted for each fish sample. The extracted features were used within Nearest Neighbor algorithm and 112 fish samples from the 6 species were classified with an overall accuracy achievement of 86%.

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“…Using a deep neural network(DNN) 's capability to learn, observe, and make a judgment with a robotic arm's ability to grasp and physically perform a task is an attractive field to be explored. Many related works such as [4], [7], [10], & [11] have done such, deploying a robotic arm to perform an objective. [7] has a robotic arm that uses computer vision to sort objects.…”

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confidence: 99%

“…A camera at the base lets the robot sort objects based on color and height when presented in front of the camera. The works of [4], [10], & [11] share a similar robotic system set up where rather than a side viewed camera; it is overhead. This camera in a permanent location provides a wide range of vision of the robotic arm's workspace from above.…”

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confidence: 99%

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Real-Time Object Detection and Grasping for a Robotic Arm

Nguyen¹

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Research and development with robotic arms have been explored for their potential to perform various physical tasks such as sorting items by colors and shapes. Prior work and research made advancements with robotic arm systems by combining them with deep neural networks (DNN) and computer-based vision to detect and interact with objects. These systems take a massive amount of time to deploy and develop due to time-consuming DNN training costs. This training cost staggers the development of robotic arms since computer vision needs a DNN to operate.Many of these systems rely on object detection to pick up items from a fixed camera overhead within their field of vision. Fixed overhead cameras compensate for the limitation of computer vision to properly locate and estimate the distances of the object in 3D space. Such that an overhead camera will assume detected objects will be on the surfaces directly in front. Research to estimate distance with computer vision relies on well-trained DNN to estimate accurately. Computer vision distance estimation has had yet to be successful with robotic arm systems. This project aims to reduce the training cost on DNN by using Transfer-Learning, to promptly deploy a well-trained DNN to a robotic arm system to detect an object and estimate its distance from a non-fixed field of vision to grasp objects in real-time. v Preface and/or Acknowledgements I want to take the time to appreciate and show my gratitude to my advisor: Dr. Qin. He is a fantastic professor and educator who inspires his students and challenges and motivates them to strive to achieve their career goals. When starting this research project, I was asked, "What is it that you want to do for a career? What is your dream?". I answered that I was interested in robotics and Deep Learning & Computer Vision which I had little knowledge about. Together we came up and developed this research project considering my interest and what he can offer his knowledge on. There were many hurdles and obstacles, especially with learning a handful of new subjects, programming language, software, developing an entire system with practically zero experience.Dr.Qin has been a beacon of knowledge and support throughout the entire phase of this research, and I am genuinely grateful to have been able to develop and learn from this challenging project as well. I also wanted to give the time to appreciate Dr.Quintero as a valuable committee member for this research. He has been an excellent resource and guidance on working with a robotic system for this project's scope. His extensive knowledge and professional experience in robotics make it an honor to showcase my work and receive his opinions and thoughts on the system. vi Table of Contents LIST OF TABLES .

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Smart Robot Arm Motion Using Computer Vision

Cited by 10 publications

References 3 publications

Development of a Color Object Classification and Measurement System Using Machine Vision

Development of a Color Object Classification and Measurement System Using Machine Vision

Classification of Serranidae Species Using Color Based Statistical Features

Real-Time Object Detection and Grasping for a Robotic Arm

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