Aiming at the deficiency of conventional traffic control method, this paper proposes a new method based on multi-agent technology for traffic control. Different from many existing methods, this paper distinguishes traffic control on the basis of the agent technology from conventional traffic control method. The composition and structure of a multi-agent system (MAS) is first discussed. Then, the step-coordination strategies of intersection-agent, segment-agent, and area-agent are put forward. The advantages of the algorithm are demonstrated by a simulation study.

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In this paper a method of on-line adaptation to illumination is proposed for mobile robot based on omni-directional in a changing illumination environment. Illumination condition is represented by an average luminance distribution of a reference object in a time series images. Illumination change is detected by computing the KL-divergence between two different distributions. A dual-threshold strategy is used to classify the current illumination into known conditions or an unknown one. According to illumination the robot decides to switch to a corresponding color calibration or learn a new one. Experiments have been carried out on the soccer robot M-TR. Experimental results show the efficiency of the proposed method.

This paper presents a novel sub-pixel corner detection algorithm for camera calibration. In order to achieve high accuracy and robust performance, the pixel level candidate regions are firstly identified by Harris detector. Within these regions, the center of gravity (COG) method is used to gain sub-pixel corner detection. Instead of using the intensity value of the regions, we propose to use corner response function (CRF) as the distribution of the weights of COG. The results of camera calibration experiments show that the proposed algorithm is more accurate and robust than traditional COG sub-pixel corner detection methods.