Sliding Window Detection Algorithm of Grabbing Safety and Grabbing Effect Based on DEM with Applications to Unmanned Bridge Grabbing Work

In the process of grabbing and issuing of the unattended system for fine particles dry bulk materials, the three-dimensional distribution of dry bulk materials is continuous, extremely irregular and fast dynamic. The selection of grabbing area is related to the grabbing safety and grabbing effect. In the extreme case of three-dimensional distribution, the improper selection of grabbing area may lead to accidents such as turning of grabs, closing rope out of groove, poor grabbing effect, or unsatisfied grab efficiency. Based on the distribution characteristics of dry bulk materials, a sliding window detection algorithm based on three-dimensional feature distribution is proposed in this paper. Before grabbing, the detection and analysis are carried out according to the distribution of materials, and the influence characteristics of relatively safe and effective distribution are judged, such as central depression, superelevation slope and other characteristics. The Digital Elevation Model (DEM) three-dimensional feature distribution model of the whole area is established, and the sliding window is further used to detect safety and efficient grab areas. The simulation results show that the sliding window detection algorithm based on DEM three-dimensional feature distribution can predict a more efficient and safe grab area according to the distribution of stacking materials, which provides a new detection method for the application of unattended bridge crane grab engineering.