The Facade Printer

Automated Paintball Turret = Large Scale Printer. 

This is a project I've wanted to build for years - my own concept was to construct a system powerful enough to paint a graphic on the side of a dorm at my Alma Mater from across campus.

Laser Command

Laser Command from Eiji Hayashi on Vimeo.

This awesome project was built by Eiji Hayashi at Carnegie Mellon University.  What stands out is the use of LED's as both display and input.  The full design is available here.

UAT Educational Robot Platform

This is a motor test of the UAT Educational Robot Platform, developed as a class project by Dan Willinger and Stephen Harper. The robot will be used as a learning platform for advanced sensor interfacing and mobile autonomous system development at the University of Advancing Technology.  This is one of the two robots that Dan and Stephen built, each costing about $1200.



Here are specs for the robot:

• Mini-ITX Motherboard with Atom 230 CPU (1.6Ghz)
• 2GB RAM
• 8GB Solid-State Storage (Compact Flash)
• Firewire
• WiFi
• PicoPSU DC-DC 150W Power Converter
• Track Chassis Kit
• Lithium-Ion Battery (25.9v, 6.4Ah)
• Phidget Motor Control Board
• Phidget USB Interface Board
• Running Ubuntu Server Edition (9.10)
• Custom Clear Lexan Chassis

RBT337 Final Project: Face Recognition

A demonstration of the final project for my course RBT337 - Digital Vision and Sensor Processing by Brittany Wilkerson and Casey Johnson. Their final project used OpenCV's face detection and SURF algorithm to identify faces in a live video feed.

RBT173 Final Project: Accelerometer Controlled Robot

A final project demonstration by Ryan Carmain, Kayla Bayens, and Leonard Hockett of their Accelerometer controlled robot. This project was for my course RBT173 - Introduction to Microcontrollers. The controller uses an accelerometer to sense the direction of gravity, which is sent over a serial connection to the robot, and translated into motor commands.

RBT173 Final Project: Text-to-Speech Twitter Robot

This is Andre Walker's final project for my course RBT173 - Introduction to Microcontrollers. The project is a text-to-speech robot that mimics emotions and reads specially tagged twitter messages out loud. It is a modified version of the GanzBot project.

RBT337 Final Project: Glyph Tracking

Mike's back, demonstrating his final project for UAT's Digital Vision and Sensor Processing course. In this video, Mike is demonstrating his SURF-based Glyph tracking system. Take it away Mike!

RBT337 Final Project: Connect 4

For their semester project in UAT's Digital Vision and Sensor Processing course, Josh Butler and Mark Stoddard implemented an excellent Connect 4 augmented reality program that warns a user if 3 pieces of the same color are placed in a row by highlighting the warning area in green. If a set of 3 is blocked, it is eliminated as a possible "win."

This first video shows the program in operation, live, raw video in the top left, augmented video in the bottom left, and color filters on the right for red and black pieces.



This second video demonstrates some of the inner workings of the program.

The bottom left pane now shows how the program scans over the all of the possible positions in the live feed, determining if the location contains a red or black piece, or is empty. This information is used to populate an array internally, which is then checked for "3 in a row."

RBT337 - Optical Flow

This is another assignment in UAT's Digital Vision and Sensor Processing course. In this laboratory, students are tasked with implementing and comparing optical flow algorithms, one using Lucas Kanade, and another using SURF.

Here is Mike Peters demonstrating optical flow using the Lucas Kanade algorithm:


And the SURF Algorithm:

RBT337 - Object Tracking

As one of the laboratory assignments in the UAT Digital Vision and Sensor Processing course, students implement the OpenCV SURF algorithm on a live video feed.  Here are some example videos of what my students produced. In the videos, the white lines indicate the tracking of matched features in one image (usually a target) to another (the live video).


(By Josh Butler)


(By Leonard Hockett)


(By Ryan Carmain)


(By Mike Peters)