Assistive Robotic Arm Control based on Brain-Machine Interface with Vision Guidance using Convolution Neural Network

Shim, Kyung-Hwan; Jeong, Ji-Hoon; Kwon, Byoung-Hee; Lee, Byeong-Hoo; Lee, Seong–Whan

doi:10.1109/smc.2019.8914058

Cited by 11 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The first five projects [1][2][3][4][5] share almost the same goal: having the robot locate objects in 3D space and then do the relocating or repositioning tasks. In contrast, the second half of the papers [6][7][8][9][10] focus on other goals, purposes, and applications. Additionally, the approach of this project is to have the depth sensor connected to the base of the robotic arm in a way that shows a front field of view for the objects around it (the reason for this approach is explained in the introduction section of this report), this approach found to be followed in papers [1][2].…”

Section: B Related Researchmentioning

confidence: 99%

Developing an Object Detection and Gripping Mechanism Algorithm using Machine Learning

Mohammad,

Kishore,

Prasetyo

2023

J. of App. Sci. & Adv. Eng.

View full text Add to dashboard Cite

Localizing and recognition of objects are critical problems for indoor manipulation tasks. This paper describes an algorithm based on computer vision and machine learning that does object detection and gripping tasks. Detection of objects is carried out using a combination of a camera and depth sensor using a Kinect v1 depth sensor. Moreover, machine learning algorithms (YOLO) are used for computer vision. The project presents a method that allows the Kinect sensor to detect objects' 3D location. At the same time, it is attached to any robotic arm base, allowing for a more versatile and compact solution to be used in stationary places using industrial robot arms or mobile robots. The results show an error of locating an object to be 5 mm. and more than 70% confidence in detecting objects correctly. There are many possibilities in which this project can be used, such as in industrial fields, to sort, load, and unload different kinds of objects based on their type, size, and shape. In agriculture fields, to collect or sort different kinds of fruits, in kitchens and cafes where sorting objects like cups, bottles, and cans can occur. Also, this project can be added to mobile robots to do indoor human services or collect trash from different places.

show abstract

Section: B Related Researchmentioning

confidence: 99%

Developing an Object Detection and Gripping Mechanism Algorithm using Machine Learning

Mohammad,

Kishore,

Prasetyo

2023

J. of App. Sci. & Adv. Eng.

View full text Add to dashboard Cite

show abstract

“…Dentro del estado del arte este tema es poco abordado por robots autónomos, sin embargo existen algunos desarrollos que usan este concepto de asistencia externa como [31,19,37,34]…”

Section: Sin Contacto -Percepción Externaunclassified

Robótica colaborativa de búsqueda y rescate, una clasificación basada en interacción física

Ulloa¹,

Valero²,

Barrientos³

2022

XLIII Jornadas De Automática: Libro De Actas: 7, 8 Y 9 De Septiembre De 2022, Logroño (La Rioja)

View full text Add to dashboard Cite

La Robótica de Búsqueda y Rescate en las últimas décadas ha sido una línea de gran crecimiento, brindando apoyo a brigadas de primera línea en situaciones pos-desastre, aportando principalmente transmisión de información desde zonas de alto riesgo o asistiendo con equipos de primeros auxilios a víctimas. El presente artículo busca establecer una clasificación entre los diferentes tipos de colaboración que pueden generarse entre robots terrestres heterogéneos para tareas de búsqueda y rescate, basado en un criterio planteado según, su interacción física, por los autores. Para ello se han realizado diferentes pruebas mediante robots con diferentes sistemas de locomoción (orugas, patas) en entornos reales que han permitido validar el criterio planteado. Entre las principales situaciones abordadas se destaca el contacto físico directo (Marsupial - Rampa) e indirecto (cables) y sin contacto físico (percepción externa - feromonas) para entornos pos-desastre. De entre las principales ventajas de la robótica colaborativa, extraídas como conclusión en base a este estudio, destacan la capacidad de maximizar las áreas de búsqueda, accediendo a zonas elevadas, explorando cráteres/socavones o asistencia entre robot a través de zonas con oclusiones, de manera similar a como lo haría una brigada de intervención humana.

show abstract

“…Finally, each object is represented as a histogram to compare it in a database and thus recognize the object. Furthermore, Shim et al in [ 103 ] suggested a hybrid algorithm for an assistive robot capable of recognizing and picking up objects from both patients and healthy people. First, the authors use a brain–machine interface to detect the user’s intention to pick up a given object.…”

Section: Algorithms Used For Objectsmentioning

confidence: 99%

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Montaño-Serrano

Jacinto‐Villegas

Vilchis-González

et al. 2021

Sensors

View full text Add to dashboard Cite

Today, computer vision algorithms are very important for different fields and applications, such as closed-circuit television security, health status monitoring, and recognizing a specific person or object and robotics. Regarding this topic, the present paper deals with a recent review of the literature on computer vision algorithms (recognition and tracking of faces, bodies, and objects) oriented towards socially assistive robot applications. The performance, frames per second (FPS) processing speed, and hardware implemented to run the algorithms are highlighted by comparing the available solutions. Moreover, this paper provides general information for researchers interested in knowing which vision algorithms are available, enabling them to select the one that is most suitable to include in their robotic system applications.

show abstract

Assistive Robotic Arm Control based on Brain-Machine Interface with Vision Guidance using Convolution Neural Network

Cited by 11 publications

References 19 publications

Developing an Object Detection and Gripping Mechanism Algorithm using Machine Learning

Developing an Object Detection and Gripping Mechanism Algorithm using Machine Learning

Robótica colaborativa de búsqueda y rescate, una clasificación basada en interacción física

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Contact Info

Product

Resources

About