Lab Scale Model Experiment of Smart Hopper System to Remove Blockages Using Machine Vision and Collaborative Robot

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.

Development of Smart and Lean Pick-and-Place System Using EfficientDet-Lite for Custom Dataset

“…The contribution of every work published previously in all the relevant domains has played a significant role in developing this work. Our pick-and-place approach is similar to a robot collecting a rock from a hopper using a vision-based image processing algorithm [7] and a robot performing pick and place operations on deformable items [8].…”

A Comparative Analysis of Cross-Validation Techniques for a Smart and Lean Pick-and-Place Solution with Deep Learning

“…In this project, a model experimental setup is created to replicate the container at the shipyard, similar to Kim [16] for smart hopper. The performance of the proposed smart and lean Pick-and-Place solution for the shipping containers are evaluated by constructing an indoor model laboratory to perform experiments using the Pick-and-Place solution on the Raspberry Pi and Universal Robot.…”