Shanxi Lanhua Qinyu Coal Mine Co., Ltd.

针对沁裕煤矿井下巷道表面特征识别困难、导航定位不精确和传感器数据融合不足等问题,进行了辅助运输车无人驾驶辅助系统的设计。采用多传感器融合理论和自动驾驶控制技术,实施包括视觉、雷达、DGPS和UWB技术在内的传感器层设计,以及基于ROS平台的控制和决策系统开发。通过整合前视摄像头、毫米波雷达、激光雷达等多种传感器,实现高精度的环境感知和定位,解决了传感器单一的局限性。开发一套完整的自动驾驶算法,包括全局路径规划、行为规划和动态避障,以优化驾驶策略和提高运输效率。系统测试结果显示,该无人驾驶辅助系统能够在复杂的矿区环境中实现高达99.5%的任务完成率,定位精度达到±5cm,有效提升了煤矿辅助运输的安全性和效率。

Aiming at the problems such as difficult in surface feature recognition, inaccurate navigation positioning, insufficient sensor data fusion and others in the downhole roadways of Shanxi Lanhua Qinyu Coal Mine, an driverless system for auxiliary transport vehicles is designed. Multi sensor fusion theory and automatic driving control technology are adopted to implement sensor layer design including vision, radar, DGPS, and UWB technology, as well as control and decision-making system development based on ROS platform. By integrating multiple types of sensors such as forward-looking cameras, millimeter wave radar, LiDAR and so on, high-precision environmental perception and positioning are achieved, solving the single limitation of the sensor; Develop a complete set of autonomous driving algorithm, including global path planning, behavior planning, and dynamic obstacle avoidance, to optimize driving strategies and improve transportation efficiency. The system test results show that this driverless system can achieve a task completion rate of up to 99.5% in complex mining area environments, with a positioning accuracy of ± 5 cm, effectively improving the safety and efficiency of coal mine auxiliary transportation.