Towards Model-based Vision Systems for Robot Soccer Teams

Since it’s beginning, Robot Soccer has been a platform for research and development of independent mobile robots and multi-agent systems, involving the most diverse areas of engineering and computer science. There are some problems to be solved in this domain, such as mechanical construction, electronics and control of mobile robots. But the main challenge is found in the areas related to Artificial Intelligence, as multi-agent systems, machine learning and computer vision. The problems and challenges mentioned above are not trivial, since Robot Soccer is dynamic, uncertain and probabilistic. A computer vision system for a Robot Soccer team must be fast and robust, and it is desirable that it can handle noise and luminous intensity variations. A number of techniques can be applied for object recognition in the domain of Robot Soccer, as described by (Grittani et al., 2000). The research of (Grittani et al., 2000) is based only on color information, as well as the research of (Weiss & Hildebrand, 2004) that uses color information to reduce the amount of information contained in each image frame through a called “relevance point filter”. Other researches uses the shape model of the objects to detect on the image, technique generally used in local vision systems. The research of (Gönner et al., 2005), for instance, detects the ball through it’s shape model projected on the image, a circumference, but still uses color-only information to recognize the robots. No matter which technique is used to solve the Robot Soccer computer vision challenge, it must be able to determine position and angle of the robots and the ball with maximum accuracy and minimal processing time possible, because the success of the strategy and control system depends on the information given by the computer vision system. This chapter extends the work presented by (Martins et al., 2006a), which considers the use of a well known image segmentation technique – the Hough Transform – to locate the mobile robots and the ball on global vision images, taking advantage of the domain characteristics – the robots and ball shape. To implement the Hough Transform technique, which is in most cases implemented in robotic systems using special hardware, only an offthe-shelf frame grabber and a personal computer are used. A new approach to interpret the Hough space is proposed, as well as the method used to recognize objects, which is based on a constraint satisfaction approach.