Robotics is a branch of engineering and science that includes mechanical engineering, electrical engineering, information engineering, computer science, and other fields. The technology itself serves the purpose of replicating human actions. One particular area of robotics and automation that has advanced very quickly in recent years is navigation. Many of today’s largest companies are establishing self-navigating robots into the lives of everyday people. Companies such as Tesla, Uber, and Google have self-driving cars travelling on the roads alongside humans, some of which are even available to consumers.

Our sponsor Dr. Michael E. Leverington is a professor at Northern Arizona University. He has taught multiple computer science courses including Operating Systems and Data Structures, and also has a masters in educational psychology. As a result of his interest in the latter, he is fascinated by the way students learn, as well as the best way for them to do so. He has always held a passion for robotics and has been following the industry for many years. He believes that the Thirty-Gallon Robot would be the perfect tool to gain interest in computer science and engineering, in addition to being a cost-effective teaching tool.

The Thirty-Gallon Robot is a three year long project to develop a robot with the ability to give guided tours of the Engineering Building. Currently, NaviBot Systems is on the second year of this project. Last year’s team created the robot with basic movement capabilities for Part One, and they named it Robot-Assisted Tours, or R.A.T. for short.

Dr. Leverington is looking for a robot that can give tours of the Engineering Building, and NaviBot Systems must produce a working navigation module for it. The following is a list of problems which we must overcome in order to implement self-navigation for R.A.T.:

• Obstacles. R.A.T. must be able to avoid any obstacles throughout the Engineering Building such as walls, people, doorways and stairs.
• Self-navigation. R.A.T. must be able to navigate to a desired location with minimal human input.
• Mapping. We must be able to create a map of the Engineering building which is readable by R.A.T.
• Localization. R.A.T. must be able to determine its location anywhere throughout the Engineering Building.
• Taking input. R.A.T. must be capable of receiving a location as input.