【摘要】 将Nelder-Mead(NM)算法与机器人主动嗅觉相结合，对室内衰减型和周期型两种时变污染源开展定位研究。首先通过计算流体动力学（CFD）模拟得到这两种时变污染源的浓度场，然后利用NM算法对其定位。结果表明，两种时变污染源的定位成功率均在80%以上。对机器人数量、响应时间和最大搜索步数这三个影响因素进行讨论，通过分析发现当室内面积为100 m2左右时，机器人数量为5个、响应时间为2 s、最大搜索步数为50步定位效果最好。更多还原

【Abstract】 In this paper, the Nelder-Mead(NM)algorithm was combined with the robot active olfaction method to locate two types of time-variant contaminant sources, which are attenuated and periodic sources,respectively. First, the concentration fields of the contaminant sources were simulated through computational fluid dynamics(CFD) simulation. Then,the time-variant contaminant sources were located using the NM algorithm. The results show that the success rates of locating the two time-variant contaminant sources are both above 80 %. Three influencing factors such as the number of robots, robot response time, and the maximum number of robot search steps were discussed.The analysis indicates that in the case of 100 m~2indoor space,time-variant sources can be best located when 5robots are working on the site with a setting of 2 seconds in robot response and a maximum number of 50 robot search steps.更多还原