融合聚类算法与改进哈里斯鹰算法的建筑机器人任务分配方法

Improved Harris Hawk algorithm combined with clustering algorithm for task assignment of construction robots

建筑机器人技术处于起步阶段,针对建筑机器人多机任务分配问题的相关研究严重不足。因此,对该问题进行分析,将其转化为多旅行商问题进行数学建模,并提出了融合改进哈里斯鹰算法与聚类算法的建筑机器人多机任务分配方法进行求解。首先根据建造任务的空间特征和建筑机器人数量利用聚类算法进行任务聚类;针对哈里斯鹰算法参数敏感与易陷入局部最优的缺点进行基于Logistic混沌映射的改进形成改进哈里斯鹰算法,根据建筑机器人的移动方式构造目标函数并在聚类的基础上进行优化求解,最终确定每个建筑机器人的任务集合与任务执行顺序。为验证该方法的有效性,利用随机生成的3∗15、5∗40、8∗70共3组不同规模的建筑机器人∗建造任务数据集进行仿真模拟,并将该方法与未融合聚类算法的GA和IHHO任务分配效果进行对比分析。结果表明,聚类能够有效解决建筑机器人任务分配问题,能够有效降低任务集合之间的空间叠加、增强优化算法的迭代收敛性能;基于Logistic混沌映射改进的算法在迭代开始与收敛时的适应度值更佳;随着问题规模的增大,融合改进哈里斯鹰与聚类算法的建筑机器人任务分配方法效果更显著,说明其更适用于解决大规模复杂的实际问题。

The construction robots technology is in its infancy,and the related research on the multi-machine task allocation problem of construction robotss is seriously insufficient.Therefore,the problem was analyzed and transformed into a multi-traveling salesman problem for mathematical modeling,and a multi-machine task allocation method for construction robotss based on improved Harris Hawk algorithm and clustering algorithm was proposed to solve the problem.Firstly,according to the spatial characteristics of the construction task and the number of construction robots,the clustering algorithm was used to cluster the tasks.Aiming at the shortcomings of Harris Hawk algorithm,which is sensitive to parameters and easy to fall into local optimum,an improved Harris Hawk algorithm based on Logistic chaotic mapping was proposed.The objective function was constructed according to the movement mode of construction robots and optimized on the basis of clustering.Finally,the task set and task execution order of each construction robots were determined.In order to verify the effectiveness of the method,three groups of randomly generated 3∗15,5∗40 and 8∗70 construction robots∗construction task data sets of different sizes were used for simulation,and the method was compared with GA and IHHO task allocation effects without fusion clustering algorithm.The results show that clustering can effectively solve the task allocation problem of construction robots,effectively reduce the spatial superposition between task sets,and enhance the iterative convergence performance of the optimization algorithm.The improved algorithm based on Logistic chaotic map has better fitness value at the beginning of iteration and convergence.With the increase of the scale of the problem,the task allocation method of the construction robots combining the improved Harris Hawk and the clustering algorithm is more effective,indicating that it is more suitable for solving large-scale and complex practical problems.