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.

Mapping of Diepoxybutane Damage in the Cytochrome b Domain of Mitochondrial DNA and the β-globin Domain of Nuclear Chicken DNA Using Quantitative PCR

Diepoxybutane (DEB), a known industrial carcinogen, reacts with DNA primarily at the N7 position of deoxyguanosine residues and creates interstrand cross-links at the sequence 5'-GNC. Since N7-N7 cross-links cause DNA to fragment upon heating, quantative polymerase chain reaction (QPCR) is being used in this experiment to measure the amount of DEB damage (lesion frequency) with three different targets-mitochondrial (unpackaged), open chromatin region, and closed chromatin region. Initial measurements of DEB damage within these three targets were not consistent because the template DNA was not the limiting reagent in the PCR. Follow-up PCR trials using a limiting amount of DNA are still in progress although initial experimentation looks promising. Sequencing of these three targets to confirm the primer targets has only been successfully performed for the closed chromatin target and does not match the sequence from NIH used to design that primer pair. Further sequencing trials need to be conducted on all three targets to assure that a mitochondrial, open chromatin, and closed chromatin region are actually being amplified in this experimental series.