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Learning Orientation Information for Robotic Soccer using Neural Nets

Robotic soccer teams using both local and global vision traditionally rely on a set of pre-determined markers (e.g., a group of small colored circles mounted on the top surface of the robot) to provide easy targets for visual analysis in order to determine the team membership, identity, and orientation of robots in the visual field. This approach requires calibration before any competition, as well as agreement in advance on color codes different enough between teams to avoid recognition errors at run-time. Even after extensive calibration, small lighting variations can cause extensive misidentification. In this paper, we examine an alternative approach: training a neural network to recognize the orientation of the robots on a team so that visual tracking can occur in real time without special markers of any kind. This paper describes the design and implementation of such an approach, and shows the results of an empirical evaluation of this approach.