Abstract
Multi-agent collaboration is the core task of multi-agent system (MAS) research. In order to explore a kind of multi-agent collaboration way with dynamic environment, this paper proposes a method based on fuzzy learning and applies it to RoboCup2D. In the RoboCup2D simulation competition, eleven players need to cooperate to win, so score becomes extremely critical. Therefore, this paper focus on shooting technique based on fuzzy control. In order to increase the scoring rate, We speculate through observation and testing that finding the correct shooting position and angle is the key. For solving the problem, this paper firstly design fuzzy controller for selecting the shooting path; and then, this paper purpose fuzzy neural network for optimizing shooting time; At last, this paper do lots of experiments to verify its effectiveness and find it does work on increasing the scoring rate. After optimization on the fuzzy control, we are fortunate to have won the national second prize in 2021 RoboCup China Open, and the goal accuracy has increased by 21.36%.
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