The Research of RoboCup2D Player Shooting Technique Based on Fuzzy Control

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%.

A Task Allocation Strategy based on Hungarian Algorithm in RoboCup Rescue Simulation

“…It has long been an active research branch in the field of distributed artificial intelligence, Multiagent system has meaningful research importance and value in problem solving, robotics and humancomputer interaction [2].The RCRS Competition assesses the teams' behavior of agents about their algorithm for planning to rescue civilians and distributed coordination in a post-earthquake city.…”