摘要
基于扩展微粒群算法模型控制群机器人协同搜索目标时,成员机器人在社会经验和自身认知,主要是社会经验引导下逐步向目标趋近.由于社会经验仅从成员机器人的认知中"选举"产生,未形式化地融合多个机器人的经验,因此文中从群机器人通信子系统在本质上属于无线传感器网络的事实出发,引入集体决策机制,改进社会经验的生成模式.用无线传感器网络中的测距定位方法来估计目标位置,并将估计值作为社会经验引入现有模型.仿真结果表明,当群体规模够大时,采用文中社会经验生成模式可使协同搜索速度得到提高.
When swarm robots are controlled cooperatively following the extended particle swarm optimization model for target search, each member robot is guided by social experience and its cognition to move step by step toward the potential goal. However, in this mechanism the social experience must be elected from the cognition of member robots rather than formal fusion. Wireless sensor network is a characteristic of swarm robotic system in aspect of physical properties, on the basis of that, a target position estimation method is employed. The range-based localization of wireless sensor network is used for target position estimation to replace social experience in the existing model. Results of simulations show that the proposed mode of social experience producing outperforms the existing ones when swarm scale is big enough, which makes cooperation search efficiency raised.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2013年第4期321-327,共7页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.60975074,61165016)
山西省自然科学基金项目(No.2009011017-1)资助
关键词
群机器人
扩展微粒群算法模型
目标搜索
机器人节点
测距定位
Swarm Robot, Extended Particle Swarm Optimization, Target Search, Robot Node,Range-Based Localization
