Hardware Didático para Mapeamento de Ambientes Indoor

Abstract

The advancement of Industry 4.0 has brought profound changes in production processes, integrating technologies such as robotics, LiDAR sensors, and cyber-physical systems. This article presents the development and evaluation of a didactic hardware system based on LiDAR and autonomous mobile robots (AMR), aimed at indoor mapping and professional training in the context of the Manaus Industrial Pole (PIM). The system, composed of an RPLIDAR A1M8 LiDAR sensor, Raspberry Pi 4, and ROS2 framework, was tested in three scenarios simulating industrial environments. The results demonstrated high accuracy in mapping, with mean absolute errors ranging between 2.5 cm and 4 cm, depending on the complexity of the environment. The system's modularity allowed its adaptation to different levels of difficulty, proving to be an effective tool for retraining workers in Industry 4.0 enabling technologies. Additionally, the proposal stood out as a low-cost and high-impact educational alternative, promoting an effective transition from theory to practice.