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Master Thesis Yasmeen Behbehani Final Draft.pdf (3.04 MB)

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Abstract Header

A Novel Multi-Sensor Fusing using a Machine Learning based Human–Machine Interface and Its Application to Automate Industrial Robots

Behbehani, Yasmeen W A H A A E
http://orcid.org/0009-0002-9641-0671
http://rave.ohiolink.edu/etdc/view?acc_num=dayton171412652681218

Abstract Details

2024, Master of Science in Electrical Engineering, University of Dayton, Electrical Engineering.
This thesis presents a novel method to control an industrial robotic arm using multiple sensors. This system consists of a hybrid brain activity and vision sensors that convey a human being’s intention and visual perception. We fuse and analyze the data from those sensors using a machine learning-based approach to automatically guide the manipulator to a designated location. We believe that this Brain–Machine–Interface (BMI) can greatly alleviate the burdensome traditional method used to program a robot (greatly aids the end-user). We experiment with different modular configurations for the brain activity information, i.e., parallelized models and what we refer to as a global model for fusing the information. We explore various machine learning and pattern recognition techniques as well as existing feature selection methods. Our experimental results show that the subject can control the robot to a destination of interest using a machine—robot–interface. We attain accuracy in the order of 99.6% when it comes to the desired motion and 99.8% for the case of deducing the desired characteristic (color) of the targeted object. These results outperform any similar existing approaches that we have researched. Moreover, in comparison to those similar operational systems, we present a unique modular configuration for brain activity interpretation and object detection mechanism that yields an overall system that is highly computationally efficient. Although, in this work, we implemented and demoed our approach using a simple pick and place demo, our work presents the basic structure underlying a system that can be efficiently used to benefit people with restricted ability to function physically (tetraplegic patients), and allowing them to perform complex and robotics related duties in an industrial setting.
Temesguen Messay-Kebede (Advisor)
Barath Narayanan (Committee Member)
Russell Hardie (Committee Member)
65 p.
Computer Engineering; Computer Science; Electrical Engineering; Engineering; Remote Sensing; Robotics
Brain Machine Interface, Machine Learning, Industrial Robotics, Sensors-Fusion, Classifiers, Similarity Distances.

Recommended Citations

Citations

  • Behbehani, Y. W. A. H. A. A. E. (2024). A Novel Multi-Sensor Fusing using a Machine Learning based Human–Machine Interface and Its Application to Automate Industrial Robots [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton171412652681218

    APA Style (7th edition)

  • Behbehani, Yasmeen. A Novel Multi-Sensor Fusing using a Machine Learning based Human–Machine Interface and Its Application to Automate Industrial Robots. 2024. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton171412652681218.

    MLA Style (8th edition)

  • Behbehani, Yasmeen. "A Novel Multi-Sensor Fusing using a Machine Learning based Human–Machine Interface and Its Application to Automate Industrial Robots." Master's thesis, University of Dayton, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=dayton171412652681218

    Chicago Manual of Style (17th edition)

dayton171412652681218
111
© 2024, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.