Recognition Of Color Targets And Key Nodes In Vision-based Soccer Humanoid Robot

RoboCup, as the world's largest robotics technology exchange platform, research focuses on multi-robot coordination and multi-agent systems in a dynamic combat environment. Teen-Size Humanoid robot Requirements that each robot is fully autonomous, In the real-time game against competition, it has the higher accuracy requirements of the running performance and travel mechanism of its hardware, and a robustness and real time of software systems. The robot vision is the main information source in humanoid soccer robot competition, whether or not to recognize the object accurately in the playground is the premise of the robot self-localization. Taking humanoid robot soccer as platform, this paper has an intensive research into the vision image processing system of teen-size humanoid soccer robot, and provides information support for self-localization.Visual image processing is divided into color image segmentation and ground feature recognition. In this paper, the main work for robot soccer focusing on above two part as follows:(1) Aiming at the current rules, game environmentand the performance requirements of the visual system, this paper rationally designs the software processes of RoboCup humanoid robot vision system. And the color image processing is divided into offline and online real-time processing.(2) Color image segmentation is devided into off-line analysis and real-time adaptive image segmentation. Off-line color analyzers is rational design, which is consisted by color histogram analysis and screen color taking tool, and identifies the off-line threshold range of different colors by analysis of off-line collected static image. For popular segmentation methods have poor real-time character and adaptability, the method introduced in this paper combines the liner threshold method and color similarity method into color image segmentation. This method can segment image in RGB color space, and save the color space transformation in other segmentations. Experimental results has proved that the method has good adaptability. It improves not only the anti-jamming capability and recognition accuracy, but also the system real-time performance.(3) Ground feature recogniton, including flags, yellow goal, blue goal and white line based on the color and structural characteristics of the playground objects, and the line fitness use modified Douglas-Peuck method. The results of this study is the premise and basis of the following Monte Carlo localization, supporting use of these features can be given characteristic reasoning, the state reset to resolve the kidnap problems, to improve the accuracy, real-time, robustness performance of Monte Carlo localization.By a large number of experiments, it can prove that above methods for online real-time image processing is adaptive and real-time, and meets the requirements of robot vision system.