A Theoretical and Technical Framework for Roboticized Mining and Hauling in Open-pit Mines
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摘要: 露天矿机器人化开采面临复杂环境数据不足、极端工况测试难、现场试验风险高、动态感知建模复杂及实验周期长等挑战. 为此, 提出基于“端边感知、平行控制”的露天矿机器人化采运智慧生产模式, 从车辆感控、车铲协同、集群调度与工程示范等多层面入手, 系统地突破提效开采机理、高精度全域感知、稳定协同控制与可靠群体管控等核心科学问题. 通过技术集成与工艺优化, 实现百吨级以上无人驾驶运输车的规模化运行, 形成我国露天煤矿高水平智能化的“双十” (10项创新技术、10项标准)、“双百” (100台车示范, 运输效率达有人系统110%)、“双千” (千台车监控平台、千小时无故障运行)中国方案, 有力支撑了我国矿山智能化绿色开采发展战略.Abstract: Roboticized mining in open-pit mines faces challenges such as insufficient data in complex environments, difficulties in testing under extreme working conditions, high risks in field trials, complexities in dynamic perception and modeling, and lengthy experimental cycles. To address these, a smart production model for robotic mining and hauling in open-pit mines based on “end-edge sensing and parallel control” has been proposed. By focusing on vehicle perception and control, shovel-vehicle collaboration, fleet scheduling, and engineering demonstrations, this model systematically addresses core scientific issues, including the mechanisms for efficient mining, high-precision holistic perception, stable and efficient collaborative control, and reliable group management. Through technological integration and process optimization, the large-scale operation of unmanned haul trucks with capacities exceeding 100 tons has been achieved. This forms the “double ten” (10 innovative technologies, 10 robotic mining standards), “double hundred” (100-vehicle demonstration, achieving 110% transportation efficiency compared to manned systems), and “double thousand” (monitoring platform for
1000 vehicles,1000 hours of mean time between failures) Chinese solution, driving high-level intelligent development in China＇s open-pit coal mines. It strongly supports the national strategy for intelligent and green mining. -
图 3 机器人化采运的“端边感知、平行控制”智慧架构
Fig. 3 “End-edge sensing and parallel control” intelligent framework for roboticized mining and hauling
图 5 基于PCA-PointPillars小目标检测技术路线
Fig. 5 PCA-PointPillars-based small target detection technical approach
图 6 矿区3D目标检测性能对比图
Fig. 6 3D object detection performance comparison chart for mining area
图 7 世界模型生成矿区驾驶场景数据
Fig. 7 Generation of world models for mining area driving scene data
图 10 矿区三维高斯重建及投影模型
Fig. 10 3D Gaussian reconstruction and projection model of the mining area
图 12 基于时空置信度的多模态传感器融合定位方法
Fig. 12 Multi-modal sensor fusion localization approach based on spatiotemporal confidence
图 14 基于BEV的多模态端边系统全域感知技术路线
Fig. 14 BEV-based multi-modal end-edge system for omnidirectional perception technology roadmap
图 16 平行仿真平台设计总体技术路线
Fig. 16 Comprehensive technological roadmap for parallel simulation platform design
图 20 虚拟地形场路径跟踪控制原理示意图
Fig. 20 Schematic of virtual terrain path tracking control principle
图 21 车铲多机协同技术
Fig. 21 Multi-machine collaborative technology for vehicle and bucket systems
图 25 无人驾驶技术应用建设体系
Fig. 25 Framework for the application and development of autonomous driving technology
图 26 黑岱沟露天煤矿无人驾驶重型运输矿卡系统作业流程图
Fig. 26 Workflow of autonomous driving heavy-duty mining truck system at Heidaigou open-pit coal mine
图 27 特殊工况下无人驾驶重型运输矿卡现场作业图
Fig. 27 On-site operation of autonomous driving heavy-duty mining trucks under special conditions
图 28 露天矿机器人化采运十项创新技术体系
Fig. 28 Ten innovative robotic mining technologies for open-pit mines
图 29 露天矿卡车无人驾驶运输技术要求标准架构
Fig. 29 Standard architecture for technical requirements for autonomous haulage systems in open-pit mining trucks
图 30 百台运输机器人运输系统编组运行示意图
Fig. 30 Schematic diagram of formation operation for a system of one hundred transportation robots
图 31 综合运输效率提升技术
Fig. 31 Technology for enhancing comprehensive transportation efficiency
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