基于计算智能的群体行为控制模型及路径生成研究

项目名称： 基于计算智能的群体行为控制模型及路径生成研究

项目编号： No.61272094

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 刘弘

作者单位： 山东师范大学

项目金额： 80万元

中文摘要： 群体运动是自然界中非常有趣并且普遍的现象。本研究拟通过对群体运动的机理做深入分析，针对现有的全局控制模型缺少表征多样个体的灵活性及多Agent局部控制模型计算开销太大的问题，提出一种群体行为的控制模型及路径生成方法。该方法采用动态小生境技术对大规模群体进行群体、组及Agent三个层次的分类，并在每个组中选择一个最优Agent作为leader; 建立Leader的认知模型、感知及协同强化学习机制，使Leader能依据环境的反馈信息，动态调整自适应函数,提高自身的自适应能力，并通过协同学习及信息共享，提高群体的适应能力；路径规划在组的层面进行，结合人工蜂群算法的多目标寻优特性和微粒群算法的群体智能特性，提高算法收敛及寻优速度，解决组中的个体的避障及碰撞问题，并有效地实现群体的聚集、分离及跟随Leader。

中文关键词： 群体运动；路径生成；人工蜂群；微粒群优化；

英文摘要： Crowd motion is a very interesting and popular phenomena in nature. This research proposes a control model of crowd behaviors and the approaches for path generation, aimed at the problems of current control model lack of flexibility for expressing individual and extremely calculate spending of multi-agent local control model, based on the deep analysis for the mechanism of crowd motion.This approach employs the dynamic niche technology to classify the large size crowd into three levels of crowd, group and agent, and selects an optimal agent in the group as the leader. It establishes the cognitive model, sesentive and cooperative reinforcement learning mechanism for leader's fitness function being adjusted dynamically according to environmental feedback information, and the adaptive ability of the leader is increased while the adaptive ability of the group is increased via cooperative learning and information sharing. The path programming is carried out at the level of group. It combines the multi-object optimizing property of artificial bee algorithm and the group intelligence property of particle swarm optimization algorithm for increasing convergence and optimizing speed, and solving the problem of individual obstacle avoidance and collision in group, and realizes collection, separation and leader-following of

英文关键词： group movement；path generation；artificial bee colony；particle swarm optimization；