VR Hand Tracking for Robotics Applications
| dc.contributor.committeeMember | Kalafatis, Stavros | |
| dc.creator | Helton, Zachary | |
| dc.date.accessioned | 2024-09-11T16:15:58Z | |
| dc.date.available | 2024-09-11T16:15:58Z | |
| dc.date.created | 2022-05 | |
| dc.date.issued | 2022-04-18 | |
| dc.date.submitted | May 2022 | |
| dc.date.updated | 2024-09-11T16:15:59Z | |
| dc.description.abstract | In industrial and factory settings, there is no truly safe way for humans to interact with robots. Large industrial machines make too much noise for vocal commands to be used as a safe method of interaction. Electrical motors, machine tools, human operators all create noise that makes vocal commands less effective in a factory than in a quiet laboratory [1]. These issues put human floor workers in unsafe situations frequently. By developing a system that can effectively recognizing track hand gestures from a human and train a robot to respond accordingly, factory safety and human-robot interaction can be revolutionized. The goal of the project is to develop and implement a program that will train a virtual robot to respond to hand gestures. Understanding concepts such as virtual and augmented reality, hand tracking, robotics, machine learning, and domain randomization will be central to the success of the project. Using Unity game design software, a virtual reality will be created in which a virtual robot will be trained to respond to human hand gestures. Thousands of environments will be simulated in VR so that a real robot will be prepared to respond to any possible command. The dataset developed in this research increased the average confidence index of a machine learning gesture recognizer by 12.45%. The end application of this research is for factory automation and other fields where the safety of human-robot interaction is a priority. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/1969.1/203357 | |
| dc.subject | Virtual Reality | |
| dc.subject | XR | |
| dc.subject | Robotics | |
| dc.subject | Machine Learning | |
| dc.subject | Factory Automation | |
| dc.subject | Hand Tracking | |
| dc.subject | Gesture Recognition | |
| dc.title | VR Hand Tracking for Robotics Applications | |
| dc.type | Thesis | |
| dc.type.material | text | |
| thesis.degree.department | Electrical & Computer Engineering | |
| thesis.degree.discipline | Electrical Engineering | |
| thesis.degree.grantor | Undergraduate Research Scholars Program | |
| thesis.degree.level | Undergraduate | |
| thesis.degree.name | B.S. |