###
DOI:
有色金属(矿山部分):2022,74(4):6-13
←前一篇   |   后一篇→
本文二维码信息
码上扫一扫!
基于自适应粒子—蚁群算法的掘进面机器人装药路径智能优化
蒋浩辰1,查正清2,段 云2
((1.北京矿冶研究总院,北京 100160; 2.矿冶科技集团有限公司,北京 100160))
Intelligent optimization of charging path for tunneling robot based on adaptive particle- ant colony algorithm
JIANG Haochen1, ZHA Zhengqing2, DUAN Yun2
((1. BGRIMM Technology Group, Beijing 100160, China; 2. BGRIMM Explosive & Blasting Technology Co., Ltd., Beijing 100160, China))
摘要
图/表
参考文献
相似文献
本文已被:浏览 8次   下载 60
投稿时间:2022-05-07    修订日期:2022-05-19
中文摘要: 针对我国地下矿山掘进面对孔装药效率低的问题,开展装药路径智能优化研究。基于蚁群优化算法,引入惯性权重自适应变化粒子群算法,求解信息素启发因子及期望值启发因子;引入自适应信息素挥发因子,根据装药路径规划问题几何特性提出自适应信息素浓度更新策略。将算法用于求解TSPLIB库中的问题,结果表明,改进算法在不同问题中求得最优解,求解精度比基本蚁群算法及几种基于蚁群算法的改进算法提高5%~9%。在实验室中利用机器人模拟掘进面对孔作业,对孔时间缩短7%。实现地下矿山掘进面对孔装药路径智能优化,对孔装药效率提升,为智能矿山建设提供参考。
Abstract:Aiming at the problem of low efficiency of charge charging in underground mining, conduct research about intelligent optimization of charging path. Based on the ant colony optimization algorithm, the particle swarm optimization algorithm with adaptive inertial weight was introduced to solve the pheromone heuristic factor α and the expected value heuristic factor β. Introduce adaptive pheromone volatile factor ρ. According to the geometric characteristics of the charge path planning problem, proposed an adaptive pheromone concentration update strategy. The algorithm is applied to solve the problems in the TSP library, and the results show that the improved algorithm can obtain the optimal solution to different problems, and the solving accuracy is 5%-9% higher than the basic ant colony algorithm and several improved algorithms based on ant colony algorithm. The drilling time was reduced by 7% by using the robot to simulate borehole facing operation in the laboratory. The intelligent optimization of charge path in the face of underground mine driving can be realized, and the efficiency of the charge path in the face of the hole can be improved, providing a reference for the construction of an intelligent mine.
文章编号:     中图分类号:TP273 ???????????????????    文献标志码:
基金项目:2018年西城区优秀人才培养资助项目(20180029);包钢钢联股份科研项目(202107)
引用文本:
蒋浩辰,查正清,段 云.基于自适应粒子—蚁群算法的掘进面机器人装药路径智能优化[J].有色金属(矿山部分),2022,74(4):6-13.
JIANG Haochen,ZHA Zhengqing,DUAN Yun.Intelligent optimization of charging path for tunneling robot based on adaptive particle- ant colony algorithm[J].NONFERROUS METALS(Mining Section),2022,74(4):6-13.

我们一直在努力打
造,精品期刊,传
播学术成果

全国咨询服务热线
86-10-63299757

杂志信息

期刊简介

相关下载

联系我们

电话:010-63299757

传真:010-63299754

QQ:XXXXXXX

Email:ysks@bgrimm.com

邮编:100160

地址:北京市南四环西路188号总部基地十八区23号楼

关注微信公众号