Development and Experimental Validation of an Educational Robotic Platform with Machine Vision and Web-Based Monitoring for Automation Teaching

The development of accessible and experimentally validated robotic systems for engineering education is a challenge, especially in academic environments where industrial manipulators are economically inaccessible. This paper presents the design, mechanical validation, and experimental evaluation of a robotic arm-based didactic module developed for the classification of objects according to color and morphology. The proposed system integrates a five-degree-of-freedom articulated configuration, a servomotor drive, motion planning with a trapezoidal speed profile, and a web-based control interface, enabling local and remote operation within an educational environment aligned with Industry 4.0 principles. The mechanical structure was designed using CAD modeling and validated through static structural analysis to ensure mechanical integrity and adequate safety factors. The selection of actuators was made considering the torque, angular velocity, and load requirements. A trapezoidal speed profile was implemented in order to ensure smooth trajectories and minimize positioning errors. Experimental validation was carried out through repetitive tests under controlled laboratory conditions, evaluating the accuracy and repeatability metrics. Statistical indicators such as mean error, standard deviation, and root mean square error (RMSE) were calculated. The results show the stable performance of the system, with low variability in multiple test cycles, confirming the viability of the proposed architecture for its implementation in automation and educational robotics laboratories. The integration of structural validation, motion control strategy, and experimental quantitative evaluation contributes to bridging the gap between theoretical teaching of robotics and its practical application, offering a scalable, low-cost platform for engineering training.