CLC number:
On-line Access: 2025-01-21
Received: 2023-11-21
Revision Accepted: 2024-02-14
Crosschecked: 2025-01-21
Cited: 0
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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 @article{title="Positioning error prediction and compensation for the multi-boom working mechanism of a drilling jumbo", %0 Journal Article TY - JOUR
凿岩台车多臂工作机构的定位误差预测与补偿机构: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: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
Reference[1]AhmadiM, NaderpourH, KheyroddinA, 2014. Utilization of artificial neural networks to prediction of the capacity of CCFT short columns subject to short term axial load. Archives of Civil and Mechanical Engineering, 14(3):510-517. ![]() [2]CaoCT, DoVP, LeeBR, 2019. A novel indirect calibration approach for robot positioning error compensation based on neural network and hand-eye vision. Applied Sciences, 9(9):1940. ![]() [3]ChenDD, WangTM, YuanPJ, et al., 2019. A positional error compensation method for industrial robots combining error similarity and radial basis function neural network. Measurement Science and Technology, 30(12):125010. ![]() [4]ChenJB, HanD, NieH, et al., 2014. Dual quaternion-based inverse kinematics of dexterous finger. Journal of Vibroengineering, 16(6):2813-2820. ![]() [5]ChiddarwarSS, BabuNR, 2010. Comparison of RBF and MLP neural networks to solve inverse kinematic problem for 6R serial robot by a fusion approach. Engineering Applications of Artificial Intelligence, 23(7):1083-1092. ![]() [6]CostamagnaE, OggeriC, SegarraP, et al., 2018. Assessment of contour profile quality in D&B tunnelling. Tunnelling and Underground Space Technology, 75:67-80. ![]() [7]CostamagnaE, OggeriC, VinaiR, 2021. Damage and contour quality in rock excavations for quarrying and tunnelling: assessment for properties and solutions for stability. IOP Conference Series: Earth and Environmental Science, 833(1):012137. ![]() [8]DuGL, LiangYH, GaoBY, et al., 2021. A cognitive joint angle compensation system based on self-feedback fuzzy neural network with incremental learning. IEEE Transactions on Industrial Informatics, 17(4):2928-2937. ![]() [9]Goetzke-PalaA, HołaA, SadowskiŁ, 2018. A non-destructive method of the evaluation of the moisture in saline brick walls using artificial neural networks. Archives of Civil and Mechanical Engineering, 18(4):1729-1742. ![]() [10]JiangGW, LuoMZ, BaiKQ, et al., 2017. A precise positioning method for a puncture robot based on a PSO-optimized BP neural network algorithm. Applied Sciences, 7(10):969. ![]() [11]KahramanS, IpekM, GuleryuzU, et al., 2006. Performance prediction of a jumbo drill in Pozanti–Ankara motorway tunnel (Turkey). Tunnelling and Underground Space Technology, 21(3-4):265. ![]() [12]KökerR, 2013. A neuro-simulated annealing approach to the inverse kinematics solution of redundant robotic manipulators. Engineering with Computers, 29(4):507-515. ![]() [13]KökerR, ÇakarT, SariY, 2014. A neural-network committee machine approach to the inverse kinematics problem solution of robotic manipulators. Engineering with Computers, 30(4):641-649. ![]() [14]LiB, TianW, ZhangCF, et al., 2022. Positioning error compensation of an industrial robot using neural networks and experimental study. Chinese Journal of Aeronautics, 35(2):346-360. ![]() [15]LiCH, YangSX, NguyenTT, 2012. A self-learning particle swarm optimizer for global optimization problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(3):627-646. ![]() [16]LiXY, LiSB, ZhouP, et al., 2022. Forecasting network interface flow using a broad learning system based on the sparrow search algorithm. Entropy, 24(4):478. ![]() [17]LiZL, ZhuB, DaiY, et al., 2022. Thermal error modeling of motorized spindle based on Elman neural network optimized by sparrow search algorithm. The International Journal of Advanced Manufacturing Technology, 121(1-2):349-366. ![]() [18]LiuDS, TanKC, GohCK, et al., 2007. A multiobjective memetic algorithm based on particle swarm optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 37(1):42-50. ![]() [19]LiuYY, XiJL, BaiHF, et al., 2021. A general robot inverse kinematics solution method based on improved PSO algorithm. IEEE Access, 9:32341-32350. ![]() [20]LuoX, ZhangYJ, ZhangL, 2021. Study of error compensations and sensitivity analysis for 6-DOF serial robot. Engineering Computations, 38(4):1851-1868. ![]() [21]NavarroJ, SegarraP, SanchidriánJA, et al., 2019. Assessment of drilling deviations in underground operations. Tunnelling and Underground Space Technology, 83:254-261. ![]() [22]RaghavanM, RothB, 1993. Inverse kinematics of the general 6R manipulator and related linkages. Journal of Mechanical Design, 115(3):502-508. ![]() [23]SethuTA, LetsebeTP, MagwazaL, et al., 2017. Introduction of drill and blast utilizing pneumatic rock-drills in a Rwandan artisanal underground mine. Journal of the Southern African Institute of Mining and Metallurgy, 117(4):313-319. ![]() [24]SongCG, YaoLH, HuaCY, et al., 2021. Comprehensive water quality evaluation based on kernel extreme learning machine optimized with the sparrow search algorithm in Luoyang river basin, China. Environmental Earth Sciences, 80(16):521. ![]() [25]TsagarisA, MansourG, 2019. Path planning optimization for mechatronic systems with the use of genetic algorithm and ant colony. IOP Conference Series: Materials Science and Engineering, 564(1):012051. ![]() [26]WangXQ, CaoJF, LiuX, et al., 2020. An enhanced step-size Gaussian damped least squares method based on machine learning for inverse kinematics of redundant robots. IEEE Access, 8:68057-68067. ![]() [27]WangYJ, FangC, JiangQM, et al., 2015. The automatic drilling system of 6R-2P mining drill jumbos. Advances in Mechanical Engineering, 7(2):504861. ![]() [28]WuD, HouGW, QiuWJ, et al., 2021. T-IK: an efficient multi-objective evolutionary algorithm for analytical inverse kinematics of redundant manipulator. IEEE Robotics and Automation Letters, 6(4):8474-8481. ![]() [29]YuDY, 2021. A new pose accuracy compensation method for parallel manipulators based on hybrid artificial neural network. Neural Computing and Applications, 33(3):909-923. ![]() [30]YuanPJ, ChenDD, WangTM, et al., 2018. A compensation method based on extreme learning machine to enhance absolute position accuracy for aviation drilling robot. Advances in Mechanical Engineering, 10(3):168781401876341 ![]() [31]ZengYF, TianW, LiDW, et al., 2017. An error-similarity-based robot positional accuracy improvement method for a robotic drilling and riveting system. The International Journal of Advanced Manufacturing Technology, 88(9-12):2745-2755. ![]() [32]ZhangDM, HannafordB, 2019. IKBT: solving symbolic inverse kinematics with behavior tree. Journal of Artificial Intelligence Research, 65:457-486. ![]() [33]ZhangL, XiaoNF, 2019. A novel artificial bee colony algorithm for inverse kinematics calculation of 7-DOF serial manipulators. Soft Computing, 23(10):3269-3277. ![]() [34]ZhangT, DuL, DaiXL, 2014. Test of robot distance error and compensation of kinematic full parameters. Advances in Mechanical Engineering, 6:810684. ![]() Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou
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