基于元胞自动机与改进A^(*)算法的砾石土摊铺实时监控作业路径规划

Path planning of gravel soil paving based on cellular automata and improved A^(*)algorithm

砾石土摊铺是心墙堆石坝施工的重要环节,其厚度和平整度是表征摊铺质量的关键指标。然而,现有的摊铺作业路径规划高度依赖人工经验,存在主观性强的缺陷,因而摊铺质量难以得到有效控制。针对上述问题,本研究提出了一种基于元胞自动机与改进A^(*)算法的砾石土摊铺实时监控作业路径规划方法。该方法通过改进A^(*)算法中的轨迹距离度量函数并引入避障冗余距离优化全局路径的规划和选取,同时结合质量评价函数优化局部动态路径规划方法,从而实现摊铺作业全局-局部混合路径规划。此外,通过建立砾石土摊铺质量元胞自动机模型,将施工机械的实时监控数据转化为仓面各网格的质量信息,实现质量信息的实时更新。工程应用表明,本研究提出的方法在保证施工效率的前提下,能够控制摊铺厚度在0.27 m左右,有效避免超厚超薄现象的发生,且平整度相较人工提高了22.6%。

Gravel paving is an important part of the construction of core rockfill dams,and its thickness and flatness are key indicators to characterize the paving quality.However,the existing paving operation path planning highly relies on manual experience and has the disadvantage of strong subjectivity,so it is difficult to effectively control the paving quality.Aiming at the above problems,this research proposes a path planning method for paving gravel soil based on cellular automata and improved A^(*)algorithm.This method optimizes the selection of the global path by improving the trajectory distance measurement function in the A^(*)algorithm and introducing the obstacle avoidance redundant distance.At the same time,it com⁃bines the quality evaluation function to optimize the local dynamic path planning method,so as to realize the global-local hybrid path planning of the paving operation.In addition,by establishing a cellular automa⁃ta model for the paving quality of gravel soil,the real-time monitoring data of the construction machinery is converted into the quality information of each grid of the working surface,so the quality information can be updated in real time.Engineering application shows that the method proposed in this study can control the paving thickness at about 0.27 m on the premise of ensuring construction efficiency,effectively avoiding the occurrence of ultra-thick and ultra-thin phenomena,and the flatness is improved by 22.6%compared with manual work.