地下铲运机自主铲装技术现状及发展趋势

doi:10.11872/j.issn.1005-2518.2021.01.162

Abstract

Abstract:

With the depletion of resources in open-pit mines，metal mines are gradually turning to deep mining. As the main equipment for loading and transporting ores in underground metal mines，the working environment of LHD（Load-Haul-Dump） is further deteriorated.The problems of harmful gases （CO，H₂S，NH₄，etc.），vibration，high stress and high temperature seriously affect people’s health.In order to protect the life health and personal safety of operators，improve the efficiency of mine operation and increase the economic benefits of mine，the intelligent mining technology of metal mine has been developed rapidly.The research on the automation of LHD has been carried out for more than 30 years，but the commercial system which has not fully realized the automatic loading can not be put into use.Because the shoveling is a dynamic and non-linear process，it is difficult to predict the change，which is the difficulty to realize the autonomous loading of LHD.In order to realize the full-automatic operation of LHD，this paper systematically studied the status quo and development trend of autonomous shoveling and loading of underground LHD，comprehensively summarized the three aspects of environmental perception and modeling，bucket trajectory control and automatic weighing，and analyzed the research status and shortcomings of its key technologies.The research results show that the environmental perception and modeling in the process of shoveling is mainly to establish the three-dimensional model of ore heap，and single type of sensor has shortcomings.Comprehensive use of the advantages of each sensor，information complementary and optimal combination can be realized.The interaction process between bucket and ore has the characteristics of dynamic change and non-linear，and the bucket trajectory control based on force is suitable for uniform medium，so it is difficult to apply in actual production.Reinforcement learning is widely used in the field of automatic control，through self-learning and adaptive environment to complete the operation task.The automatic weighing system can measure the effective load of the bucket and adjust the state of the ore in the bucket to prevent the ore from falling.There is a big gap in the research on the automatic weighing system at home and abroad.Compared with foreign countries，the automatic weighing technology in China is relatively backward，at the same time，the automatic weighing technology is mostly used in the ground loader，less in the underground.At present，there is also a big gap between China and foreign countries in the research of self loading technology of underground scraper.Strengthening cooperation in related fields and carrying out field test of LHD are the key points to promote the development of LHD in China.

Key words: underground LHD, automatic loading, perceptual modeling, trajectory control, weighing system, artificial intelligence

CLC Number:

TD52

Dan JIANG,Liguan WANG. Present Situation and Development Trend of Self-loading Technology for Underground Load-Haul-Dump[J].Gold Science and Technology, 2021, 29(1): 35-42.

Figures/Tables 3

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References 0

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传感器类型	实现原理	优点	缺点
视觉摄像机	根据光照构建物体深度图	解析度高、视场广，价格低，算法丰富，具有通用性、精度高	受光照影响，硬件投入大，不能实时建模
三维激光扫描仪	根据分析时间推断仪器到矿堆的距离	受光线的影响小，测量范围广，响应快，精度高，体积小	价格高昂，数据处理复杂，单一位置信息无法识别物体
超声波测距仪	根据飞行时间推断仪器到矿堆的距离	受光线的影响小，价格低，体积小，可用于恶劣环境	数据采集慢，解析度低，范围窄

方法	实现方式	优点	缺点
基于力反馈的铲斗轨迹控制	通过传感器参数调节铲斗运动	实时反映铲斗受力情况	适用均匀介质，复杂交互不适用
基于学习的铲斗轨迹控制	通过训练相关参数，自动调节铲斗铲装	自适应调节铲斗运动	需要大量数据，应用范围窄

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Present Situation and Development Trend of Self-loading Technology for Underground Load-Haul-Dump

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