基于SLAM技术的矿区巷道巡检机器人路径规划优化

Path Planning Optimization of Mine Roadway Inspection Robot Based on SLAM Technique

针对矿区巷道环境复杂、道路狭窄等特点,提出了一种基于SLAM(SimultaneousLocalizationandMapping)技术的矿区巷道巡检机器人路径规划优化方法,实现机器人的自主定位和地图构建。采用激光雷达、RGB-D相机等多种传感器相融合,获取矿区三维点云数据,并使用SLAM算法实时构建矿区三维地图。同时,通过配准当前获取的点云数据与已构建的地图,实现机器人在矿区内的自主定位。针对矿山中存在的狭窄、弯曲、分支等复杂环境,提出了一种增量式A~*优化算法用于路径规划。该算法在传统A~*算法的基础上,引入了路径平滑、走廊宽度约束等优化策略,能生成满足矿区复杂环境约束的平滑可行路径。算法采用增量式方式更新,只需对改变的局部区域重新进行路径搜索,大大减少了整体路径规划的计算耗时。通过试验验证该算法性能,结果表明:与传统路线规划方法相比,该算法能够快速、精准地完成巡检任务,为矿区巷道巡检机器人推广应用提供了参考。

Aiming at the characteristics of complex roadway environment and narrow road in mining area,a path planning optimization method of roadway inspection robot based on SLAM(Simultaneous Localization and Mapping)technique is pro-posed to realize the autonomous positioning and map construction of the robot.Firstly,a variety of sensors such as LiDAR and RGB-D cameras are fused to obtain 3D point cloud data of mining area,and SLAM algorithm is used to construct 3D map of mining area in real time.At the same time,through the registration of the currently obtained point cloud data and the built map,the robot can realize the autonomous positioning in the mining area.Secondly,an incremental A∗optimization algorithm is proposed for path planning in view of the narrow,bending,branching and other complex environments in mines.Based on the traditional A∗algorithm,the algorithm introduces optimization strategies such as path smoothing and corridor width constraints,which can generate smooth feasible paths that meet the constraints of complex mining environment.The algorithm is updated in-crementally,and only needs to research the changed local area,which greatly reduces the calculation time of the overall path planning.The experimental results show that compared with the traditional route planning method,the algorithm can complete the inspection task more quickly and accurately,which provides a reference for the popularization and application of mine road-way inspection robot.