School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China.
Key Laboratory for Micro/Nano Technology and System of Liaoning Province, School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China.
Sensors (Basel). 2021 Jun 25;21(13):4355. doi: 10.3390/s21134355.
Electric shovels have been widely used in heavy industrial applications, such as mineral extraction. However, the performance of the electric shovel is often affected by the complicated working environment and the proficiency of the operator, which will affect safety and efficiency. To improve the extraction performance, it is particularly important to study an intelligent electric shovel with autonomous operation technology. An electric shovel experimental platform for intelligent technology research and testing is proposed in this paper. The core of the designed platform is an intelligent environmental sensing/perception system, in which multiple sensors, such as RTK (real-time kinematic), IMU (inertial measurement unit) and LiDAR (light detection and ranging), have been employed. By appreciating the multi-directional loading characteristics of electric shovels, two 2D-LiDARs have been used and their data are synchronized and fused to construct a 3D point cloud. The synchronization is achieved with the assistance of RTK and IMU, which provide pose information of the shovel. In addition, in order to down-sample the LiDAR point clouds to facilitate more efficient data analysis, a new point cloud data processing algorithm including a bilateral-filtering based noise filter and a grid-based data compression method is proposed. The designed platform, together with its sensing system, was tested in different outdoor environment conditions. Compared with the original LiDAR point cloud, the proposed new environment sensing/perception system not only guarantees the characteristic points and effective edges of the measured objects, but also reduces the amount of processing point cloud data and improves system efficiency. By undertaking a large number of experiments, the overall measurement error of the proposed system is within 50 mm, which is well beyond the requirements of electric shovel application. The environment perception system for the automatic electric shovel platform has great research value and engineering significance for the improvement of the service problem of the electric shovel.
电动铲运机广泛应用于矿业等重工业领域。然而，电动铲运机的性能常常受到复杂的工作环境和操作人员熟练度的影响，这会影响安全性和效率。为了提高开采性能，研究具有自主作业技术的智能电动铲运机尤为重要。本文提出了一种用于智能技术研究和测试的电动铲运机实验平台。所设计平台的核心是一个智能环境感知/感知系统，其中采用了多个传感器，如 RTK（实时动态）、IMU（惯性测量单元）和 LiDAR（光探测和测距）。通过感知电动铲运机的多向加载特性，使用了两个 2D-LiDAR，并对其数据进行同步和融合，以构建 3D 点云。通过 RTK 和 IMU 辅助实现同步，提供铲斗的姿态信息。此外，为了对 LiDAR 点云进行下采样，以方便更高效地进行数据分析，提出了一种新的点云数据处理算法，包括基于双边滤波的噪声滤波器和基于网格的数据压缩方法。所设计的平台及其传感系统在不同的户外环境条件下进行了测试。与原始 LiDAR 点云相比，所提出的新环境感知/感知系统不仅保证了被测物体的特征点和有效边缘，而且减少了处理点云数据的量，提高了系统效率。通过进行大量实验，该系统的总体测量误差在 50mm 以内，远远超过了电动铲运机应用的要求。自动电动铲运机平台的环境感知系统对于提高电动铲运机的服务问题具有重要的研究价值和工程意义。
