Designing a Remote Navigation System

This project involves the design and implementation of a global electronic tracking system intended for use by trans-oceanic vessels, using the technology of the U.S. Government's Global Positioning System (GPS) and a wireless connection to a networked computer. Traditional navigation skills are being replaced with highly accurate electronics. GPS receivers, computers, and mobile communication are becoming common among both recreational and commercial boaters. With computers and advanced communication available throughout the maritime world, information can be shared instantaneously around the globe. This ability to monitor one's whereabouts from afar can provide an increased level of safety and efficiency. Current navigation software seldom includes the capability of providing upto-the-minute navigation information for remote display. Remote access to this data will allow boat owners to track the progress of their boats, land-based organizations to monitor weather patterns and suggest course changes, and school groups to track the progress of a vessel and learn about navigation and science. The software developed in this project allows navigation information from a vessel to be remotely transmitted to a land-based server, for interpretation and deployment to remote users over the Internet. This differs from current software in that it allows the tracking of one vessel by multiple users and provides a means for two-way text messaging between users and the vesseI. Beyond the coastal coverage provided by cellular telephones, mobile communication is advancing rapidly. Current tools such as satellite telephones and single-sideband radio enable worldwide communications, including the ability to connect to the Internet. If current trends continue, portable global communication will be available at a reasonable price and Internet connections on boats will become more common.

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.