Mechanical Engineering ETDs
Publication Date
Fall 12-15-2024
Abstract
This dissertation enhances human-computer interactions through integration of pattern recognition, Deep Learning (DL), and robotics fundamentals with C#-UnityEngine platform Augmented Reality (AR) headsets. This document begins by introducing a method to integrate Canny algorithm with AR headsets’ platform for crack detection. It then develops a method for converting pixels to engineering scales for crack measurements. Additionally, to reduce the runtime of image processing, the research proposes an automatic Region-Of-Interest (ROI) selection algorithm. Next, the integration of DLs with AR headsets allows for complex image recognition tasks. This thesis demonstrates the value of integrating human awareness of the environment with robotic tasks by creating AR interfaces for robot programming that allow users to reduce randomness of robots’ sampling-based path-planning. The ultimate goal of this research is to evaluate enhancement of human-machine collaboration in robotics and visual inspection by creating the mentioned AR platforms and integrations.
Degree Name
Mechanical Engineering
Level of Degree
Doctoral
Department Name
Mechanical Engineering
First Committee Member (Chair)
Fernando Moreu
Second Committee Member
Claus Danielson
Third Committee Member
Abdullah Mueen
Fourth Committee Member
Charles Farrar
Document Type
Dissertation
Language
English
Recommended Citation
Malek, Kaveh. "INTEGRATION OF HUMAN WITH IMAGE-BASED MODELS, AND ROBOTICS FUNDAMENTALS USING WEARABLE IMMERSIVE PLATFORMS." (2024). https://digitalrepository.unm.edu/me_etds/276
