基于多传感器数据融合的煤矿移动机器人自主导航研究

Research on Autonomous Navigation of Coal Mine MobileRobot Based on Multi-sensor Data Fusion

针对煤矿井下环境空间狭窄、非结构化以及能见度低等状况,移动机器人环境感知不足、定位精度不高和自主导航性能差等问题,提出了基于多传感器数据融合的煤矿移动机器人自主导航方法,通过有效融合激光雷达、相机和IMU采集数据信息,采用改进SURF算法快速图像特征匹配,结合粒子群算法提供的最佳位姿,构建高精准环境地图,并通过路径规划获取最佳安全可行的路径,实现移动机器人的自主导航。通过实验测试,验证了多传感器数据融合的自主导航系统可行安全性和稳定性,能有效提升煤矿移动机器人的鲁棒性。

Aiming at the problems of narrow,unstructured,and low visibility in the underground environment of coal mines,as well as insufficient environmental awareness,poor positioning accuracy,and poor autonomous navigation performance of mobile robots,a method for autonomous navigation of coal mine mobile robots based on multi-sensor data fusion was proposed.By effectively fusing data collected by radars,cameras,and IMUs,an improved SURF algorithm was adopted for fast image feature matching,combining the optimal posture provided by particle swarm optimization algorithm,a high-precision environmental map was constructed,and the best safe and feasible path was obtained through path planning to achieve autonomous navigation of mobile robots.Through experimental testing,it was verified that the autonomous navigation system based on multi-sensor data fusion was feasible,safe,stable,and effectively improved the robustness of coal mine mobile robots.