基于VSPSO和A-G网络的掘进机动态路径规划  

Dynamic path planning of roadheader based on VSPSO and A-G net

作　　者：杨健健[1] 唐至威 王子瑞 韩松 郭一楠 吴淼[1] YANG Jian-jian;TANG Zhi-wei;WANG Zi-rui;HAN Song;GUO Yi-nari;WU Miao(School of Mechanical Electronic and Information Engineering,China University of Mining and Technology (Beijing),Beijing 100083,China;School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221116,China)

机构地区：[1]中国矿业大学(北京)机电与信息工程学院,北京100083 [2]中国矿业大学信息与控制工程学院,江苏徐州221116

出　　处：《控制与决策》2019年第3期642-648,共7页Control and Decision

基　　金：国家973计划项目(2014CB046306);中央高校基本科研业务费专项基金项目(800015FC)

摘　　要：为解决部分断面悬臂式掘进机行进路径规划问题,实现掘进机的无人化掘进,提出基于变异自适应粒子群算法(VSPSO)和行为规则栅格网络(A-G)的掘进机动态路径规划方法.通过分析掘进机行进特征和煤矿井下巷道特征,建立基于行为规则的栅格网络模型和代价模型,给定代价函数的类型及耗费系数的取值范围,以巷道模拟数据为基础,通过所提出的VSPSO算法和6种改进型PSO算法进行掘进机行进路径规划并对结果进行比较.比较结果表明,在测试函数下, VSPSO算法收敛速度更快、收敛精度更高,在行为规则栅格网络模型下, VSPSO算法的收敛速度与精度最高,且能够规划出符合掘进机行为特征的最优行进路径.This paper proposes a dynamic path planning method in order to solve the problem of traveling path planning of partial section cantilever roadheader and realize unmanned tunneling of roadheader. This method is based on variation self-adaptive particle swarm optimization algorithm(VSPSO) and action-grid(A-G). Based on the analysis of roadheader traveling characteristics and coal mine roadway characteristics, a grid network model and a cost model based on behavior rules are established. By setting the type of cost function and the range of consumption coefficient in the model, and based on the roadway simulation data, we complete a comparative analysis of the proposed VSPSO algorithm and six improved PSO algorithm in the roadheader path planning model. The experimental results show that under the test function, the VSPSO algorithm converges faster and converges more accurately. The VSPSO algorithm has the highest convergence speed and accuracy under the behavior rule grid network model, and can plan the optimal traveling path according to the behavior characteristics of roadheader.

关 键 词：掘进机 行为约束 环境建模 行为规则栅格网络 粒子群算法 路径规划 

分 类 号：TD421[矿业工程—矿山机电] TP242[自动化与计算机技术—检测技术与自动化装置]