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矿山无人驾驶系统关键技术研究综述

Review of key technologies for unmanned driving systems in mining

  • 摘要: 矿山作为矿产资源的重要生产场所,在推动国家经济建设和社会发展的过程中发挥着不可替代的作用。然而,传统矿山运输存在作业环境恶劣、安全风险高和人力成本大等问题,亟须引入新一代技术手段加以改进。矿山无人驾驶系统可以通过融合自动化、信息化与智能化技术,实现矿山运输的全流程无人化操作,不仅显著提升生产效率和安全水平,还具备在复杂地形、极端气候和动态工况条件下连续作业的能力。围绕矿山无人驾驶系统,分别从技术架构设计与工程实践应用2个角度出发,系统梳理近年来该领域国内外的研究现状与发展趋势;重点对环境感知、路径规划、车辆控制以及多车调度等关键技术进行深入分析与归纳,涵盖动态高清地图更新与异构传感器协同感知方法、不规则道路路径生成与优化机制、适应复杂工况的车辆控制策略以及面向系统整体效率的多车协同调度方案;全面揭示当前矿山无人驾驶系统面临的挑战,包括:感知精度退化导致地图更新、目标检测与定位能力受限,混行与交互不确定性导致路径规划难以收敛,多变地形与工况下控制器难以保持鲁棒性与精度,控制偏差积累与反馈不及时限制调度系统全局优化能力等方面;同时结合技术发展瓶颈与产业需求,提出矿山运输从“传统模式”向“智能化”再到“完全无人驾驶”演进的发展路径与研究建议,从而实现运输效率优于人工驾驶的水平。

     

    Abstract: As a crucial site for mineral resource production, mines play an irreplaceable role in driving national economic development and social progress. However, traditional mine transportation faces several challenges, including harsh working environments, high safety risks, and significant labor costs, necessitating the adoption of next-generation technologies for improvement. Unmanned driving systems in mining integrate automation, informatization, and intelligent technologies to enable full-process unmanned operation of mine transportation. These systems significantly enhance production efficiency and safety levels, while maintaining the capability to operate continuously under complex terrain, extreme weather, and dynamic working conditions. Focusing on unmanned driving systems in mining, recent domestic and international research status and development trends in this field are systematically reviewed from two perspectives: technical architecture design and engineering application practices. Besides, an in-depth analysis and summarization are conducted on key technologies such as environmental perception, path planning, vehicle control, and multi-vehicle scheduling. These include dynamic high-definition map updating and collaborative perception with heterogeneous sensors, irregular road path generation and optimization, control strategies adaptive to complex conditions, and multi-vehicle coordinated scheduling schemes for overall system efficiency. Moreover, the various challenges faced by current unmanned driving systems in mining are comprehensively revealed, including degraded perception accuracy limiting map updates, object detection, and localization capabilities; uncertainty in mixed-traffic interactions impeding path planning convergence; difficulties in maintaining controller robustness and precision under varying terrains and conditions; and the accumulation of control deviations and delayed feedback hampering global optimization in scheduling systems. In light of existing technological bottlenecks and industrial demands, a developmental roadmap and research recommendations are proposed for advancing mine transportation from “traditional models” to “intelligent systems” and ultimately to “fully unmanned driving”, aiming to achieve transportation efficiency that surpasses manual driving.

     

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