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On-line Access: 2025-01-21

Received: 2023-11-21

Revision Accepted: 2024-02-14

Crosschecked: 2025-01-21

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Songyong LIU

https://orcid.org/0000-0002-2801-7969

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Journal of Zhejiang University SCIENCE A

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Positioning error prediction and compensation for the multi-boom working mechanism of a drilling jumbo


Author(s):  Yuming CUI, Songyong LIU, Zhengqiang SHU, Zhenli LV, Lie LI

Affiliation(s):  School of Mechatronic Engineering, Jiangsu Normal University, Xuzhou221116, China; more

Corresponding email(s):  lsycumt@163.com

Key Words:  Multi-boom rock-drilling jumbo; Kinematic model; Neural network optimization; Positioning error prediction


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Yuming CUI, Songyong LIU, Zhengqiang SHU, Zhenli LV, Lie LI. Positioning error prediction and compensation for the multi-boom working mechanism of a drilling jumbo[J]. Journal of Zhejiang University Science A,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.A2300594

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Abstract: 
A rock-drilling jumbo is the main piece of tunneling equipment used in the energy and infrastructure industries in various countries. The positioning accuracy of its drilling boom greatly affects tunneling efficiency and section-forming quality of mine roadways and engineering tunnels. In order to improve the drilling-positioning accuracy of a three-boom drilling jumbo, we established a kinematics model of the multi-degree-of-freedom (multi-DOF) multi-boom system, using the improved Denavit-Hartenberg (D-H) method, and obtained the mapping relationship between the end position and the amount of motion of each joint. The error of the inverse kinematics calculation for the drilling boom is estimated by an analytical method and a global search algorithm based on particle swarm optimization (PSO) for a straight blasting hole and an inclined blasting hole. On this basis, we propose a back-propagation (BP) neural network optimized by an improved sparrow search algorithm (ISSA) to predict the positioning error of the drilling booms of a three-boom drilling jumbo. In order to verify the accuracy of the proposed error compensation model, we built an automatic-control test platform for the boom, and carried out a positioning error compensation test on the boom. The results show that the average drilling-positioning error was reduced from 9.79 to 5.92 cm, and the error was reduced by 39.5%. Therefore, the proposed method effectively reduces the positioning error of the drilling boom, and improves the accuracy and efficiency of rock drilling.

凿岩台车多臂工作机构的定位误差预测与补偿

作者:崔玉明1,刘送永2,束正强2,吕振礼2,李烈1
机构:1江苏师范大学,机电工程学院,中国徐州,221116;2中国矿业大学,机电工程学院,中国徐州,221116
目的:钻臂定位精度是影响矿山巷道和工程隧道掘进效率和断面成型质量的重要因素。本文旨在探讨钻臂各关节误差的分布规律并通过算法对钻臂末端位置的误差进行预测和补偿,以提高三臂凿岩台车的钻孔定位精度。
创新点:1.针对直爆孔和斜爆孔,分别采用解析法和基于粒子群优化的全局搜索算法对钻臂逆运动学计算误差进行了估算;2.用改进的麻雀搜索算法优化反向传播(BP)神经网络来预测三臂凿岩台车钻臂的定位误差。
方法:1.采用改进的Denavit-Hartenberg法建立多自由度多臂系统的运动学模型,得到末端位置与各关节运动量之间的映射关系;2.基于粒子群优化的全局搜索算法对钻臂逆解误差进行估算;3.采用改进的麻雀搜索算法优化BP神经网络来预测三臂凿岩台车钻臂的定位误差;4.搭建钻臂自动控制试验平台,并进行钻臂定位误差补偿试验。
结论:1.直爆孔和斜爆孔可以通过不同的方法求出对应钻臂关节的逆解;2.采用改进的麻雀搜索算法优化BP神经网络来预测三臂凿岩台车钻臂的定位误差,可将平均钻孔定位误差从9.79 cm降至5.92 cm,使误差降低了39.5%。

关键词组:多臂凿岩台车;运动学模型;神经网络优化;定位误差预测

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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