A Multi-Sensor Environmental Perception System for an Automatic Electric Shovel Platform

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.

[Retracted] Optimization and Application of Information Visualization Design Based on Image Symbol under the Guidance of Feature Integration Theory

“…In [14] the researchers discussed an integral aspect of shovels with allowing movement technology to boost extraction performance.…”

“…In [ 14 ] the researchers discussed an integral aspect of shovels with allowing movement technology to boost extraction performance. Their study proposes an “electric shovel” investigational platform for intelligent equipment development and testing.…”

Research on Path Planning and Trajectory Tracking of an Unmanned Electric Shovel Based on Improved APF and Preview Deviation Fuzzy Control

“…Li designed a perception system for the shovel and tested it on a 1:10 scale shovel experimental platform. Through the combination of real-time kinematic (RTK) and inertial measurement unit (IMU), the system yields information with centimeter-level accuracy [5]. Bijo Sebastian proposed a physics-based autonomous navigation path planning architecture for tracked vehicles that considers slippage and terrain slope.…”

Real-Time Recognition of Loading Cycles’ Process Based on Electric Mining Shovel Monitoring

“…At present, the methods used to recognize the loading equipment's operation status include the multisensor device [5,6], machine vision technology [7], and the device's own parameters, for example, the displacement of the equipment mechanism [8,9], the hydraulic oil pump pressure [10], the motor voltage and current [11,12], and the vibration spectrum of the chassis [13,14].…”