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Location estimation of autonomous driving robot and 3D tunnel mapping in underground mines using pattern matched LiDAR sequential images

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International Journal of Mining Science and Technology 2021年第5期31卷 779-788页

作者： Heonmoo Kim yosoon choi Department of Energy Resources Engineering Pukyong National UniversityBusan 48513Republic of Korea

In this study,a machine vision-based pattern matching technique was applied to estimate the location of an autonomous driving robot and perform 3D tunnel mapping in an underground mine *** autonomous driving robot continuously detects the wall of the tunnel in the horizontal direction using the light detection and ranging(Li DAR)sensor and performs pattern matching by recognizing the shape of the tunnel *** proposed method was designed to measure the heading of the robot by fusion with the inertial measurement units sensor according to the pattern matching accuracy;it is combined with the encoder sensor to estimate the location of the *** addition,when the robot is driving,the vertical direction of the underground mine is scanned through the vertical Li DAR sensor and stacked to create a 3D map of the underground *** performance of the proposed method was superior to that of previous studies;the mean absolute error achieved was 0.08 m for the X-Y axes.A root mean square error of 0.05 m^(2)was achieved by comparing the tunnel section maps that were created by the autonomous driving robot to those of manual surveying.

关键词： Pattern matching Location estimation Autonomous driving robot 3D tunnel mapping Underground mine

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Self-Driving Algorithm and Location Estimation Method forSmall Environmental Monitoring Robot in Underground Mines

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Computer Modeling in Engineering & Sciences 2021年第6期127卷 943-964页

作者： Heonmoo Kim yosoon choi Department of Energy Resources Engineering Pukyong National UniversityBusan48513Korea

In underground mine environments where various hazards exist,such as tunnel collapse,toxic gases,the application of autonomous robots can improve the stability of exploration and efficiently perform repetitive exploratory *** this study,we developed a small autonomous driving robot for unmanned environmental monitoring in underground *** developed autonomous driving robot controls the steering according to the distance to the tunnel wall measured using the light detection and ranging sensor mounted on the robot to estimate its location by simultaneously considering the measured values of the inertial measurement unit and encoder *** addition,the robot autonomously drives through the underground mine and performs environmental monitoring using the temperature/humidity,gas,and particle sensors mounted on the *** a result of testing the performance of the developed robot at an amethyst mine in Korea,the robot was found to be able to autonomously drive through tunnel sections with∼28 m length,∼2.5 m height,and∼3 m width *** average error of location estimation was approximately 0.16 *** environmental monitoring sensors,temperature of 15–17◦C,humidity of 42%–43%,oxygen concentration of 15.6%–15.7%,and particle concentration of 0.008–0.38 mg/m3 were measured in the experimental area,and no harmful gases were *** addition,an environmental monitoring map could be created using the measured values of the robot’s location coordinates and environmental factors recorded during autonomous driving.

关键词： Underground mine environmental monitoring autonomous driving geographic information system

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Optimized functional linked neural network for predicting diaphragm wall deflection induced by braced excavations in clays

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Geoscience Frontiers 2022年第2期13卷 34-51页

作者： Chengyu Xie Hoang Nguyen yosoon choi Danial Jahed Armaghani School of Environment and Resources Xiang tan UniversityXiangtan 411105China Department of Surface Mining Mining FacultyHanoi University of Mining and GeologyDuc ThangBac Tu LiemHanoiViet Nam Innovations for Sustainable and Responsible Mining(ISRM)Group Hanoi University of Mining and GeologyDuc ThangBac Tu LiemHanoiViet Nam Department of Energy Resources Engineering Pukyong National UniversityBusan 48513Republic of Korea Department of Urban Planning Engineering Networks and SystemsInstitute of Architecture and ConstructionSouth Ural State University76Lenin ProspectChelyabinsk 454080Russia

Deep excavation during the construction of underground systems can cause movement on the ground,especially in soft clay *** high levels,excessive ground movements can lead to severe damage to adjacent *** this study,finite element analyses(FEM)and the hardening small strain(HSS)model were performed to investigate the deflection of the diaphragm wall in the soft clay layer induced by braced *** geometric and mechanical properties of the wall were investigated to study the deflection behavior of the wall in soft ***,1090 hypothetical cases were surveyed and simulated based on the HSS model and FEM to evaluate the wall deflection *** results were then used to develop an intelligent model for predicting wall deflection using the functional linked neural network(FLNN)with different functional expansions and activation *** the FLNN is a novel approach to predict wall deflection;however,in order to improve the accuracy of the FLNN model in predicting wall deflection,three swarm-based optimization algorithms,such as artificial bee colony(ABC),Harris’s hawk’s optimization(HHO),and hunger games search(HGS),were hybridized to the FLNN model to generate three novel intelligent models,namely ABC-FLNN,HHO-FLNN,*** results of the hybrid models were then compared with the basic FLNN and MLP *** revealed that FLNN is a good solution for predicting wall deflection,and the application of different functional expansions and activation functions has a significant effect on the outcome predictions of the wall *** is remarkably interesting that the performance of the FLNN model was better than the MLP model with a mean absolute error(MAE)of 19.971,root-mean-squared error(RMSE)of 24.574,and determination coefficient(R^(2))of ***,the performance of the MLP model only obtained an MAE of 20.321,RMSE of 27.091,and R^(2)of ***,the results also indicated that the proposed

关键词： Diaphragm wall deflection Braced excavation Finite element analysis Clays Meta-heuristic algorithms Functional linked neural network

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Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms

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Geoscience Frontiers 2021年第3期12卷 458-472页

作者： Chengyu Xie Hoang Nguyen Xuan-Nam Bui yosoon choi Jian Zhou Thao Nguyen-Trang School of Environment and Resources Xiangtan UniversityXiangtan 411105China Institute of Research and Development Duy Tan UniversityDa Nang 550000Viet Nam Department of Surface Mining Mining FacultyHanoi University of Mining and Geology18 Pho VienDuc Thang wardBac Tu Liem districtHanoi 100000Viet Nam Center for Mining Electro-Mechanical researchHanoi University of Mining and Geology18 Pho VienDuc Thang wardBac Tu Liem districtHanoi 100000Viet Nam Department of Energy Resources Engineering Pukyong National UniversityBusan 48513Republic of Korea School of Resources and Safety Engineering Central South UniversityChangsha 410083HunanChina Division of Computational Mathematics and Engineering Institute for Computational ScienceTon Duc Thang UniversityHo Chi Minh City 700000Viet Nam Faculty of Mathematics and Statistics Ton Duc Thang UniversityHo Chi Minh City 700000Viet Nam

Blasting is well-known as an effective method for fragmenting or moving rock in open-pit *** evaluate the quality of blasting,the size of rock distribution is used as a critical criterion in blasting operations.A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental ***,this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters,as well as the efficiency of blasting operation in open ***,a nature-inspired algorithm(i.e.,firefly algorithm-FFA)and different machine learning algorithms(i.e.,gradient boosting machine(GBM),support vector machine(SVM),Gaussian process(GP),and artificial neural network(ANN))were combined for this aim,abbreviated as FFA-GBM,FFA-SVM,FFA-GP,and FFA-ANN,***,predicted results from the abovementioned models were compared with each other using three statistical indicators(e.g.,mean absolute error,root-mean-squared error,and correlation coefficient)and color intensity *** developing and simulating the size of rock in blasting operations,136 blasting events with their images were collected and analyzed by the Split-Desktop *** which,111 events were randomly selected for the development and optimization of the ***,the remaining 25 blasting events were applied to confirm the accuracy of the proposed ***,blast design parameters were regarded as input variables to predict the size of rock in blasting ***,the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench *** the models developed in this study,FFA-GBM provided the highest accuracy in predicting the size of fragmented *** other techniques(i.e.,FFA-SVM,FFA-GP,and FFA-ANN)yielded lower computational stability and ***,the FFA-GBM model can be used as a powerful

关键词： Mine blasting Rock fragmentation Artificial intelligence Hybrid model Gradient boosting machine Meta-heuristic algorithm

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