Route-based vibration data collection remains a manual task in industrial condition monitoring: technicians walk predefined equipment routes with portable collectors on weekly to quarterly schedules, which is labor-intensive and introduces operator-to-operator variability. This project develops an autonomous mobile robot platform that replicates the route definition and execution logic of commercial vibration analysis software. A TurtleBot 4 running ROS 2 is driven by a custom browser-based control panel (connected over rosbridge) that supports manual teleoperation, live SLAM mapping, waypoint capture, and grouping of waypoints into named, color-coded inspection routes with per-point wait, skip, and arrival-error parameters. Route order is optimized as a Traveling Salesman Problem using exact permutation search for small waypoint sets and a nearest-neighbor with 2-opt heuristic for larger ones, while navigation is executed through the Nav2 stack (Dijkstra-based global planning on an inflated costmap, DWB local control, and behavior-tree recovery). The system logs arrival error at every measurement point, captures a camera snapshot on arrival, and exports per-route results to Excel. Validation in a controlled indoor environment demonstrates repeatable multi-route execution and provides the navigation foundation for mounting a vibration sensing payload.

This project develops an immersive VR learning system integrated with an LLM-powered conversational avatar for real-time educational assistance. The system enables in-environment question answering within a virtual classroom, addressing the lack of open-ended interaction in existing VR tools. A Unity-based prototype with a local LLM was evaluated through case studies and baseline comparison with a PC-based AI system.

This project develops and evaluates an advanced Mixed Reality (MR) training platform for hurricane and flood response, aimed at enhancing preparedness for both first responders and community members. Built using Unity and deployed on the Meta Quest 3, the system overlays dynamic virtual hazards nto real-world environments.

This research project focuses on the development and deployment of a real-time object detection system for Mixed Reality (MR) and Virtual Reality (VR) environments using the Meta Quest 3S platform. Leveraging the newly released AI Building Blocks provided in Meta SDK v83+ and the Meta XR MR Utility Kit v83+.

The Vertical-Horizontal Illusion (VHI) biases perceived geometric proportions across visual displays. This study introduces the Square Recognition Test (SRT), a unified method to measure perceived squareness across devices. A within-subject study (N = 24) on PC, mobile, and AR HMDs shows a consistent bias toward slightly wider-than-tall rectangles (PSE ≈ 1.02-1.04), indicating device-invariant VHI effects. Two AR variants were compared, and curved-canvas rendering produced lower bias and higher accuracy than flat-canvas AR, pointing to rendering geometry as an actionable lever for perceptual calibration in XR interface design. Accepted to ASME IDETC/CIE 2026 (Houston, TX).

An AR headset-based prototyping system has been developed to offer a clear and intuitive digital visualization for the purpose of exploring the potential applications of smart factories. This project presents a case study of a maintenance application in a CNC machine shop for technicians.

This project demonstrates the potential of utilizing digital twin technology with an Augmented Reality interface for improved maintenance procedures. By presenting a demo system of a repair process, this work aims to highlight the benefits of using AR for clear and intuitive visualization of the device being repaired, resulting in improved accuracy and ease of instruction for technicians.

This work proposed a novel system, HAR2bot, for path planning tasks in complex environments. The proposed system effectively balances the communication between the operator and the robot to minimize the cognitive burden associated with the design loop, leading to improved task efficiency and accuracy.

This project proposed and developed an intuitive and immersive AR-based interface for cobot (AUBO i5) programming, aiming to address the challenges in performing path planning tasks in complex environments.