Abstract: Aiming at the problems of the odor source localization algorithms by using multi-robot in indoor turbulent environment, such as the low utilization rate of historical concentration information and the lack of mechanism to adjust the global and local search, a multi-robot cooperative search algorithm combing Brain Storm Optimization (BSO) algorithm and upwind search was proposed. Firstly, the searched location of robot was initialized as an individual and the robot position was taken as the center for clustering, which effectively used the guiding role of historical information. Secondly, the upwind search was defined as an individual mutation operation to dynamically adjust the number of new individuals generated by the fusion of selected individuals in a class or two classes, which effectively adjusted the global and local search methods. Finally, the odor source was confirmed according to the two indexes of concentration and persistence. In the simulation experiments under two environments with and without obstacles, the proposed algorithm was compared with three kinds of swarm intelligent multi-robot odor source localization algorithms. The experimental results show that, the average search time of the proposed algorithm is reduced by more than 33% and the location accuracy is 100%. The proposed algorithm can effectively adjust the global and local search relations of robot, and locate the odor source quickly and accurately.

Key words: odor source localization, turbulent environment, multi-robot, Brain Storm Optimization (BSO) algorithm, upwind search

摘要： 针对现有室内湍流环境下多机器人气味源搜索算法存在历史浓度信息利用率不高、缺少调节全局与局部搜索的机制等问题，提出头脑风暴优化（BSO）算法与逆风搜索结合的多机器人协同搜索算法。首先，将机器人已搜索位置初始化为个体，以机器人位置为中心聚类，有效利用了历史信息的指引作用；然后，将逆风搜索作为个体变异操作，动态调节选中一个类中个体或两个类中个体融合生成新个体的数量，有效调节了全局和局部搜索方式；最后，根据浓度和持久性两个指标对气味源进行确认。在有障碍和无障碍两个环境中将所提算法与三种群体智能多机器人气味源定位算法进行定位对比仿真实验，实验结果表明，所提算法的平均搜索时间减少33%以上，且定位准确率达到100%。该算法能够有效调节机器人全局和局部搜索关系，快速准确定位气味源。

关键词: 气味源定位, 湍流环境, 多机器人, 头脑风暴优化算法, 逆风搜索