Machine learning and small robot navigation

Developing successful navigation and mapping strategies is an essential part of autonomous robot research. However, hardware limitations often make for inaccurate systems. This project serves to investigate efficient alternatives to mapping an environment, by first creating a mobile robot, and then applying machine learning to the robot and controlling systems to increase the robustness of the robot system. My mapping system consists of a semi-autonomous robot drone in communication with a stationary Linux computer system. There are learning systems running on both the robot and the more powerful Linux system. The first stage of this project was devoted to designing and building an inexpensive robot. Utilizing my prior experience from independent studies in robotics, I designed a small mobile robot that was well suited for simple navigation and mapping research. When the major components of the robot base were designed, I began to implement my design. This involved physically constructing the base of the robot, as well as researching and acquiring components such as sensors. Implementing the more complex sensors became a time-consuming task, involving much research and assistance from a variety of sources. A concurrent stage of the project involved researching and experimenting with different types of machine learning systems. I finally settled on using neural networks as the machine learning system to incorporate into my project. Neural nets can be thought of as a structure of interconnected nodes, through which information filters. The type of neural net that I chose to use is a type that requires a known set of data that serves to train the net to produce the desired output. Neural nets are particularly well suited for use with robotic systems as they can handle cases that lie at the extreme edges of the training set, such as may be produced by "noisy" sensor data. Through experimenting with available neural net code, I became familiar with the code and its function, and modified it to be more generic and reusable for multiple applications of neural nets.

Metamorphic epiphany -- 3D animation

As far back as I can remember, I have always been interested in studio art. Whether it be painting, drawing, printmaking, or photography, it has consistently been a part of my life. Upon enrolling in Colby, I became interested in computers and decided to major my undergraduate college career in Computer Science. Not forgetting past interests, I continued my studio art education, taking several classes within the Art department. In due time, I began combining interests and began studying Computer Graphics and Design. With limited resources in this field at Colby, the majority of my computer graphic education and experience has been done on my own time apart from regular classroom work. As time progressed, so did my interests. Starting with simple image manipulation of digitally scanned photographs, I moved on to Web Page design, eventually leading to Desktop Publishing. Ultimately, I wanted to take a step further and expand my overall computer graphic knowledge by learning 3D modeling and animation. With even fewer resources in 3D animation at Colby, I perceived having trouble finding the information and tools I would need to gain the necessary skills for this new field. The Senior Scholars program gave me the opponunity to find and acquire the necessary tools to pursue my interest. This program also allowed me to devote the proper amount of time required for learning these new tools.