Contents I. Introduction
Using mobile robots to challenge college students with competition assignments is a common practice in many engineering schools. In many cases, students are challenged to engineer some robotics tasks without the capability of proper robot localization. For example, many sumo robots and similar competitions are either designed to work without accurate position information or the use of expensive industrial solutions [1]. Mobile robot positioning in micro platforms is a critical component to a student's understanding of mobile robotic based solutions. Once the localization of participant robots is established, the student's gain room for using their creativity and imagination to compose complex mobile robotic tasks. Accurate positioning of mobile robots allows for collaborative tasks among teamed robots, development of realtime strategies and numerous other choices to explore by students. This in return helps enhancing the student's understanding of concepts in mobile robot applications. Current options available for micro platform positioning systems is limited to a few different methods. In the following a few different solutions are outlined. A feasible solution is to first utilize a dedicated structure that mounts various LIDAR and IR based transmitters and receivers with the objective to capture time of arrival data that is combined with environmental data and allows for the measurement of mobile robot locations. Another method is based on using radio frequencies to measure the received signal strength as an indicator of distance and position. This approach however is highly non-linear in nature to compute position readings. Both of the aforementioned solutions are rather expensive due to proprietary algorithms used along with costly hardware sold by limited number of companies, [2]. Hence, this paper presents a typical inexpensive academic robotic competition environment setup with cost effective navigation and mapping solutions for robots in a micro-environment. The outline of this paper is as follows: At first, a suitable mobile robot hardware is selected with a specific configuration in order to conduct an experimental task. This is followed by presenting three different navigation and mapping techniques. In particular, (1) Odometry, (2) Array of beacons and (3) Computer vision, which are tested, evaluated, and discussed in this paper. A cost comparison is presented with commercially available alternative solutions.
