基于混合IWO—PSO算法的掘进机截割轨迹规划方法

田劼; 银晓琦; 文艺成

doi:10.13272/j.issn.1671-251x.2021050018

基于混合IWO—PSO算法的掘进机截割轨迹规划方法

doi: 10.13272/j.issn.1671-251x.2021050018

田劼^1,2,
银晓琦^1,2,
文艺成^1,2

1.
1. 中国矿业大学(北京) 机电与信息工程学院, 北京 100083;
2.
2. 煤矿智能化与机器人创新应用应急管理部重点实验室, 北京 100083

基金项目:

中央高校基本科研业务费专项资金资助项目(2021YJSJD17)。

详细信息

作者简介:
田劼(1982-)，女，山西太原人，副教授，博士，主要研究方向为掘进智能控制，E-mail：tianj@cumtb.edu.cn。

中图分类号: TD632
计量
- 文章访问数: 133
- HTML全文浏览量: 15
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- 被引次数: 0
出版历程
- 收稿日期: 2021-05-10
- 修回日期: 2021-11-26
- 刊出日期: 2021-12-20

Method of cutting trajectory planning of roadheader based on hybrid IWO-PSO algorithm

1.
School of Mechanical Electronic and Information Engineering, China University of Mining and Technology(Beijing), Beijing 100083, China;
2.
Key Laboratory of Coal Mine Intelligent and Robot Innovative Application Emergency Management Department, Beijing 100083, China

摘要

摘要: 针对掘进机截割轨迹规划方法准确度低、对掘进设备损耗大的问题，提出了一种基于混合IWO（杂草优化）-PSO（粒子群优化）算法的掘进机截割轨迹规划方法。将截割断面环境分为单夹矸、双夹矸和多夹矸3种，对相应断面进行栅格化并建立栅格地图，采用二值膨胀法对不规则夹矸进行膨胀化处理，并采用混合IWO-PSO算法在3种断面环境中进行轨迹规划。混合IWO-PSO算法以IWO算法中的种子扩散方式为基础，对初始群体进行扩散，在竞争排斥前允许所有个体自由繁殖，使寻优空间的多样化得到有效保障；同时采用PSO算法中的位置迭代更新方式对繁殖的种子位置进行迭代更新，利用群体经验和个体经验对粒子位置进行及时调整，有效提高了算法寻优深度和速度。仿真结果表明，基于混合IWO-PSO算法得到的掘进机截割轨迹长度、二次挖掘栅格数和截割能耗均小于标准PSO算法，对障碍夹矸的规避能力优于标准PSO算法。通过EBZ135型掘进机进行断面截割试验，结果表明，巷道断面成形左侧、右侧、两侧边界误差最大值分别为30，20，50 mm，相对误差分别在2%，1.4%，1.7%内，可满足不同巷道断面环境下的有效避障和成形要求。
- 掘进机 /
- 截割轨迹规划 /
- 截割能耗 /
- 夹矸 /
- 混合IWO-PSO算法 /
- 杂草优化 /
- 粒子群优化
Abstract: In order to solve the problems of low accuracy and large loss of heading equipment in cutting trajectory planning method of roadheader, a cutting trajectory planning method of roadheader based on hybrid IWO (invasive weed optimization)-PSO (particle swarm optimization) algorithm is proposed. The cutting section environments are divided into three types, namely single gangue, double gangue and multi-gangue, and the corresponding sections are rasterized and the grid map is established. The irregular gangue is expanded by using binary expansion method. The hybrid IWO-PSO algorithm is used for trajectory planning in the three types of section environments. The hybrid IWO-PSO algorithm is based on the seed diffusion method in IWO algorithm, which diffuses the initial population and allows all individuals to reproduce freely before competitive exclusion, thus effectively ensuring the diversity of optimization space. The position iterative update method in PSO algorithm is also used to iteratively update the reproduced seed positions, and the particle positions are adjusted in time by using group experience and individual experience to improve the optimization depth and speed of the algorithm effectively. The simulation results show that the length of cutting trajectory, the number of secondary excavation grids and the cutting energy consumption of the roadheader based on the hybrid IWO-PSO algorithm are smaller than those of the standard PSO algorithm, and the capability to avoid the obstacle and gangue is better than that of the standard PSO algorithm. The section cutting test is carried out by EBZ135 roadheader, and the results show that the maximum errors of the left side, right side and both sides of the roadway section forming are 30, 20 and 50 mm respectively, and the relative error is within 2%, 1.4% and 1.7% respectively, which can meet the requirements of effective obstacle avoidance and forming under different roadway section environments.
- roadheader /
- cutting trajectory planning /
- cutting energy consumption /
- gangue /
- hybrid IWO-PSO algorithm /
- invasive weed optimization /
- particle swarm optimization

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参考文献(14)

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