Project

# Title Team Members TA Documents Sponsor
19 SCARA Drawing Robot
 Bingzhe Wei
Chenghao Duan
Tianhao Chi
 Dongwei Shi design_document0.pdf
final_paper0.pdf
Members: Bingzhe Wei (bwei6) Tianhao Chi (tchi3) Chenghao Duan (cduan2)

Title: SCARA Drawing Robot

Description:

We propose to develop a drawing robot based on an SCARA robot arm https://www.youtube.com/watch?v=vKD20BTkXhk. Overall system processing flow will be as follows:
1. User inputs image to image-processing program on PC.
2. Image processing with program on PC:
a. Style transfer via deep neural networks
b. Clustering of similar colors
c. Pixel fill algorithm to convert to vector strokes
3. Vectors sent to microcontroller program via USB or similar.
4. Microcontroller program does inverse kinematics and commands motors as necessary.

The combination of the SCARA design and stepper motors will enable a very stable and fast drawing platform, while the proposed image processing algorithms enable multiple, arbitrary styles and provide high-quality visual effects. We expect steppers will reduce the need for control.

Proposed circuit will contain an USB-to-Serial converter IC, ATmega644 microcontroller, stepper motor controllers, and optical phototransistors for feedback control, as well as associated support circuitry. We use an ATmega644 with 4K of RAM, double that of ATmega328 as found in regular Arduinos to ensure enough capacity for inverse kinematics, while power will be supplied via a standard wall plug adapter that outputs 12V DC.

Our team also has access to GPUs for training deep neural networks.

Our team members have taken the following courses:

ECE 470 - Robotics
ECE 486 - Control System
SE 423 - Mechatronics
ECE 515 - Control Theory and Design

ECE 547 - Topics in Image Processing - Deep Learning
CS 598 PS - Machine Learning For Signal Processing

Wireless IntraNetwork

Daniel Gardner, Jeeth Suresh

Wireless IntraNetwork

Featured Project

There is a drastic lack of networking infrastructure in unstable or remote areas, where businesses don’t think they can reliably recoup the large initial cost of construction. Our goal is to bring the internet to these areas. We will use a network of extremely affordable (<$20, made possible by IoT technology) solar-powered nodes that communicate via Wi-Fi with one another and personal devices, donated through organizations such as OLPC, creating an intranet. Each node covers an area approximately 600-800ft in every direction with 4MB/s access and 16GB of cached data, saving valuable bandwidth. Internal communication applications will be provided, minimizing expensive and slow global internet connections. Several solutions exist, but all have failed due to costs of over $200/node or the lack of networking capability.

To connect to the internet at large, a more powerful “server” may be added. This server hooks into the network like other nodes, but contains a cellular connection to connect to the global internet. Any device on the network will be able to access the web via the server’s connection, effectively spreading the cost of a single cellular data plan (which is too expensive for individuals in rural areas). The server also contains a continually-updated several-terabyte cache of educational data and programs, such as Wikipedia and Project Gutenberg. This data gives students and educators high-speed access to resources. Working in harmony, these two components foster economic growth and education, while significantly reducing the costs of adding future infrastructure.