The robot vision system is the critical component of the soccer robot, in football competition, robot perceive the most of the information from the vision system. Because of the variable illumination conditions, the traditional image segmentation method based on color information is not satisfactory. Based on the color information and shape information of the object, this paper proposes a object recognition algorithm that combine color image segmentation with edge detection. This algorithm implement image segmentation use color information in the HSV color space obtain the pixel of the object, then use this pixel implement edge detection to recognize the object. Experiments show that this algorithm can recognize the object exactly in the different illumination conditions, satisfy the requirement of the competition.
A gait control system is designed for walking a slope movement according to the features of biped robot, Two BP networks are introduced into train the walking trajectory of robot and the optimal trajectory is generated by the new evolutionary approach based on particle swarm optimization. Additionally, online environmental information is collected as neural network input, and necessary joint trajectory is output for the movement. Simulations with Matlab and experiments on NAO humanoid robot testify the efficiency of the method.
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