受湍流影响,室内通风环境下的烟羽分布表现出波动变化且不连续的特性;在一些角落处,较大的漩涡会产生长时间的局部浓度极值区;另外室内的障碍物也会改变烟羽的分布状况.因此室内有障碍通风环境下的机器人气味源搜索问题变得很复杂.本文提出了基于概率适应度函数的粒子群优化(Probability-fitness-function based particle swarm optimization,P-PSO)算法并用于多机器人气味源搜索.P-PSO算法的特点是采用概率而非确定数来表达适应度函数值.针对气味源搜索问题,P-PSO算法的适应度函数值由贝叶斯和变论域模糊推理估计的气味源概率表达.为验证提出的搜索策略,构建了对应实际边界条件的室内通风环境的烟羽模型.仿真研究证明了本文提出的P-PSO搜索算法用于解决气味源搜索问题的可行性.
In this paper,we address the odor source localization (OSL) problem in wireless sensor network.The OSL has pot...
Yong Zhang is with the School of Electrical Engineering and Automation,Tianjin University,Tianjin,300072,China.Qing-Hao Meng is with the School of Electrical Engineering and Automation,Tianjin University,Tianjin,300072,China.Yu-Xiu Wu is with the School of Electrical Engineering and Automation,Tianjin University,Tianjin,300072,China.Ming Zeng is with the School of Electrical Engineering and Automation,Tianjin University,Tianjin,300072,China.