###### Abstract  
In this study, we propose a smart hopper system that automatically unblocks obstructions caused by rocks dropped into hoppers at mining sites. The proposed system captures RGB (red green blue) and D (depth) images of the upper surfaces of hopper models using an RGB-D camera and transmits them to a computer. Then, a virtual hopper system is used to identify rocks via machine vision-based image processing techniques, and an appropriate motion is simulated in a robot arm. Based on the simulation, the robot arm moves to the location of the rock in the real world and removes it from the actual hopper. The recognition accuracy of the proposed model is evaluated in terms of the quantity and location of rocks. The results confirm that rocks are accurately recognized at all positions in the hopper by the proposed system. smart hopper; RGB-D camera; robot image processing; machine vision technology +
Footnote †: journal: Applied  
Acticle Lab Scale Model Experiment of Smart Hopper System to Remove Blockages Using Machine Vision and Collaborative Robot  
Robot. April. 20221257990  
In the mining field, research using various ICTs is being conducted, and commercial products are also being developed. [PERSON] et al. [33] simulated the truck-loader transport system of an open-pit mine on AnyLogic software and visualized the results intuitively. Haile Gold Mine in South Carolina in the USA measured and predicted the density of the slurry supplied and discharged to the cyclone accurately [34]. A simulation was conducted in the Ban Houayxai Gold-Silver Mine to estimate the expected output with respect to changes in the blasting design factor and predict the metal recovery rate and prospective improvement of the crushing efficiency [35]. [PERSON] et al. [36] learned big data on the truck movement system at the mine site, optimized it through various machine learning techniques, and predicted productivity indicators. Previous studies have been conducted to analyze and visualize data in virtual space, such as building a database, real-time monitoring, and simulation using ICTs. However, few studies have been conducted to control the physical space using the analysis results in the virtual space. In this study, the image processing-based vision system is used to recognize objects of rocks placed in hopper equipment, one of the mineral processing facilities used in mining sites. The hopper is a funnel-shaped device that screens dropped rocks and serves as an entrance into the crusher. At a mining site, the hopper equipment is usually installed on the upper part of the crusher that crushes the rocks and provides an avenue through which the rocks input into the gyratory crusher pass before being crushed [37]. Figure 1a illustrates the process by which the transported rocks are dispensed from the truck into the hopper. In general, the wide entrance of the hopper facilitates the sequential input of rocks dispensed from the truck into the gyratory crusher. However, when a large number of rocks or a large rock is suddenly input into the hopper, a clogging phenomenon called \"jamming\" could occur (Figure 1b). In hoppers implemented in existing mining sites, manual intervention either by hand or with mechanical help, e.g., a crane, is necessary to remove the blockage. This requires work to be halted while the blockage is removed. Moreover, a direct manual approach toward the entrance of a crusher can be dangerous. These issues can be resolved by utilizing image recognition and robot technology in the hopper to resolve the clogging phenomena automatically, thereby improving the productivity and stability of mining work. This study aims to propose a smart hopper system at a mining site using an RGB-D (Red Green Blue-Depth) camera, machine vision technology, and a collaborative robot.