基于头脑风暴优化算法的多机器人气味源定位

发布时间：2019-03-24 11:11

【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。
[Abstract]:In the presence of indoor turbulence, the existing multi-robot odor source search algorithms have some problems, such as low utilization of historical concentration information, lack of mechanism to regulate global and local search, and so on. A multi-robot cooperative search algorithm based on brainstorming optimization (BSO) algorithm and headwind search is proposed. Firstly, the robot's search position is initialized as an individual, and the robot's position is taken as the center of clustering, which makes effective use of the guidance of historical information. Then, taking the headwind search as an individual mutation operation, the number of new individuals is dynamically adjusted to select the individuals in one class or the individuals in two classes, and the global and local search modes are effectively adjusted. Finally, the source of odour was confirmed according to the two indexes of concentration and persistence. The simulation results show that the average search time of the proposed algorithm is reduced by more than 33%, and the simulation results show that the proposed algorithm is compared with three swarm intelligent multi-robot odor source localization algorithms in barrier-free and barrier-free environments, and the simulation results show that the average search time of the proposed algorithm is reduced by more than 33%. The accuracy of location is 100%. The algorithm can effectively adjust the global and local search relationship of the robot and locate the smell source quickly and accurately.
【作者单位】：河北工业大学电气工程学院;河北工业大学计算机科学与软件学院;
【基金】：天津市科技计划项目(14JCYBJC18500,14ZCDGSF00124)~~
【分类号】：TP242

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