DIDACTIC HARDWARE FOR MAPPING INDOOR ENVIRONMENTS

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 paper 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, consisting of an A1M8 RPLIDAR 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 modularity of the system allowed it to be adapted to different levels of difficulty, proving to be an effective tool for the requalification of workers in enabling technologies of Industry 4.0. In addition, the proposal stood out as a low-cost, high-impact educational alternative, promoting an effective transition from theory to practice.