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RoboCupSoccer中多机器人协作截球策略
引用本文:蔡剑怀,李茂青,吴顺祥.RoboCupSoccer中多机器人协作截球策略[J].计算机工程与应用,2010,46(16):1-5.
作者姓名:蔡剑怀  李茂青  吴顺祥
作者单位:1.厦门大学 信息科学与技术学院,福建 厦门 361005 2.厦门大学 生命科学学院,福建 厦门 361005
基金项目:国家自然科学基金,航空科学基金,厦门大学"985工程"二期信息创新平台项目 
摘    要:截球策略是决定机器人足球队比赛能力的重要因素。由于信息噪音、命令执行误差、多异构类型和随机异构参数等因素的影响,单纯基于个体技术的截球决策并不一定可靠。首先采用数学解析方法建立截球个体技术模型,利用牛顿迭代法求解最快截球周期、截球点和基本命令队列;然后基于BP神经网络描述两个截球周期相比的截球成功概率;最后机器人基于自己和同伴的角色关系、截球成功概率和截球点所在球场区域进行协作截球决策。实验结果显示基于角色的协作截球效果明显改善。基于此策略的机器人足球队在比赛中取得不错的成绩。

关 键 词:RoboCupSoccer  截球模型  BP神经网络  角色
收稿时间:2010-2-22
修稿时间:2010-4-7  

Collaboration interception strategy of multi robots in RoboCupSoccer
CAI Jian-huai,LI Mao-qing,WU Shun-xiang.Collaboration interception strategy of multi robots in RoboCupSoccer[J].Computer Engineering and Applications,2010,46(16):1-5.
Authors:CAI Jian-huai  LI Mao-qing  WU Shun-xiang
Affiliation:1.School of Information Science & Technology,Xiamen University,Xiamen,Fujian 361005,China 2.School of Life Sciences,Xiamen University,Xiamen,Fujian 361005,China
Abstract:Interception strategy is an important factor for the competitive ability of robot soccer team.Due to the effect of the information noise,command implementation error,multi-heterogeneous types and random heterogeneous parameters,the interception strategy simply based on individual skill is not necessarily reliable.Firstly,mathematical analysis method is adopted to establish the interception individual skill model,and Newton iteration method is used to get the fastest interception cycle,interception point and the basic command queue.Then BP neural network is adopted to describe the interception success probability comparing two interception cycles.Finally the robot makes the interception decision collaboratively considering the allocation of roles,the interception success probability and the playfield interception point locates.The experiment results show the improvement of the rolebased collaboration interception.The robot soccer team with such strategy gets good results in the competitions.
Keywords:RoboCupSoccer  interception model  BP neural network  role
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