Storage System for Automated Object Classification Based on Color Analysis - Michael Guerra; Santiago Proaño; Angélica Quito; Samuel Peña-Bautista (Engineering Proceedings)
This article introduces a compelling study on the design and implementation of a storage system for automated object classification using color analysis. Despite notable advancements in robotic systems, the precise and reliable color-based classification in uncontrolled environments remains an ongoing challenge. The primary aim of this research is to develop an economical prototype that integrates color sensor and robotic arm to significantly enhance the efficiency of object-sorting processes across a wide range of industries. This study showcases the system’s impressive high accuracy rates of 97% for red, 96% for blue, and 98% for white objects in controlled settings. However, it also highlights the drop in accuracy to 76% for red, 64% for blue, and 68% for white objects in uncontrolled environments. These findings underscore the importance of maintaining consistent environmental conditions to optimize system performance, while also pointing to promising opportunities for modernization and semi-automation within the pharmaceuticals, food, and manufacturing sectors.