Side-wall is the static obstacle in soccer robot game, reasonably making use of the Side-wall can improve soccer robot
competitive ability. As a kind of artificial life, the Side-wall processing strategy of soccer robot is influenced by many
factors, such as game state, field region, attacking and defending situation and so on, each factor also has different
influence degree, so, the Side-wall behavior selection is an intelligent selecting process. From the view point of human
simulated, based on the idea of Side-wall processing priority[1], this paper builds the priority function for Side-wall
processing, constructs the action predicative model for Side-wall obstacle, puts forward the Side-wall processing strategy,
and forms the Side-wall behavior selection mechanism. Through the contrasting experiment between the strategy applied
and none, proves that this strategy can improve the soccer robot capacity, it is feasible and effective, and has positive
meaning for soccer robot stepped study.
The robotics soccer game is a standard task platform, the soccer robot is one form of the artificial life body. Based on this platform, in terms of the action-selection model of artificial life, carries a comparable and valid study. Firstly, this paper analyses the action layer and the action type of the soccer robot. Then, according to the behavior selection model of artificial life body based on priority degree, improves the action selection model of the soccer robot. Finally, builds the decision-making system of soccer robot based on the model, and this system has been applied in the actual games of soccer robot, the result indicates that it is workable and efficient.
The question may be posed as to which fusion algorithm is most suitable for irrelevant images in a Virtual Photographing System (VPS).In the present paper fusion image quality evaluating methods are proposed to be the key. In this paper, a new method is developed by first combining objective and subjective methods. Then, three different methods are used for objective evaluation, i.e., information entropy evaluation, cross grads evaluation for interested objects and an approaching index evaluation for edge transition smoothness. Five image fusion algorithms are presented. Experimental results prove that this hierarchical evaluated method is effective and more suitable for VPS. The conclusions of the quantitative evaluation and human vision are similar.
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