Research On Key Issues Of Color Omni-directional Vision For Soccer Robots

The robot soccer, as a hard nut to crack, is a research focus in the field of the robot control, machine vision, artificial intelligence, and multi-robots systems. Middle-Size League, as a crucial competition in RoboCup, provides a standard testing environment for the color omni-directional vision techniques. The dissertation made an investigation on the color classification, the feature extraction, and the sensor model establishment in the application of the color omni-directional vision system in the field of the Middle-Size RoboCup competition.Firstly, the dissertation designed and realized a color catadioptric omni-directional vision system according to the need of Middle-Size League in RoboCup. The system features in the use of combined distortionless omni-mirror and the digital color camera in order to obtain digital color panoramic image of the competition environment effectively. Accordingly, the dissertation expounded the principles, the characteristics of the main components and the measurement of the main parameters, and decided the effective range of distance measurement of the system.Secondly, aiming at enhancing the ability of color classification for the color panoramic image, the dissertation proposed a combined color space CLUT (Color Look-Up Table) color classification method based on the linear classifiers. The method solved the problem that the current CLUT method is hard to distinguish the similar colors due to the influence of inaccurate choices of color space and threshold. The improved CLUT method applied the linear classifier in pattern recognition to the establishment of CLUT mapping relationship. Meanwhile, HSI and YUV color spaces were synchronously employed to increase the similar colors classification ability. The experiments indicated that the combined color space classification method, based on the linear classifiers, is convenient to establish CLUT and to take effect. Additionally, the method satisfied the real-time requirement of the color panoramic image classification in RoboCup with an effective way to distinguish similar colors.Thirdly, the dissertation investigated the feature extraction method of the color panoramic image in the RoboCup competition. It analyzed the effects of the inconsistent light condition in area and time on the feature extraction of the RoboCup competition field. Furthermore, by way of the color transition detection, it demonstrated that the white lines on the field can be detected reliably without color classification, and the touch points between the blue or yellow gate and the field can also be detected reliably with the unsatisfied color classification results. The principle of the two kinds of detection was to make use of the relative distribution of various colors in the color space, to decide the change of luminance (for the white lines) and the hue (for the blue or yellow gate) with the scan-line detection method, and to estimate the character of the change according to the color structuralized information of the field. The dissertation, for the two field features respectively, proposed the principles and the arithmetic which were proved effective and robust in the experiments.Finally, based on the results of the color classification and the feature extraction, the dissertation explored the way to establish the color omni-vision sensor model in the RoboCup competition with the particle filter localization method. The color omni-vision sensor, as the environment sensor of particle filter localization method, detected the chromatic transitions of interest as the observation information. Moreover, the dissertation analyzed the color omni-vision sensor model, indicated the mathematics description and the calculation method of the main parameters. In addition, the dissertation clarified the factors to affect the precision of the sensor model by comparing the robot localization information from the sensor model and the real position and orientation. The experiment results indicated that the color omni-vision sensor model, with great robustness, can overcome the invalidation of partial observed information. In particular, the employment of double look-up tables increased the speed of particle belief calculation and the practicability of the model.