Abstract
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.
Highlights
A new automatic electric shovel platform has been designed in this work at Dalian University of Technology (DUT)
The processing of point cloud filtering and down-sampling is a standard method, the combination of processing methods and the personalized setting of parameters can achieve a more significant performance in engineering based on ensuring accuracy and efficiency, which is useful for the development of engineering equipment and provide theoretical and practical basis
This experimental site has the characteristics of a large scene range, a wide field of view and a stable GNSS signal reception, which can meet the needs of large-scale detection of LiDAR, and the experimental site has landmark objects, such as buildings, trees, fences, etc., which can facilitate achieving the error analysis of the scan results and the actual size of the objects
Summary
Xie et al used a small hydraulic excavator to build a three-dimensional environment sensing/perception system based on a single-line LiDAR and a camera [14] These works lack pose information and data fusion. Constrained by complex operating environments affected by various weather conditions, a single sensor solution is not capable of fully describing the environment information required for a safe and reliable operation of automatic electric shovels To overcome these issues, multiple sensors, such as LiDAR, inertial navigation and RTK, can be implemented to work coherently to provide enriched data information [22,23,24,25,26].
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