The choice of structural parameters in the design of artificial neural networks is generally based on trial-and-error procedures. They are regularly estimated based on the previous experience of the researcher, investing large amounts of time and processing resources during network training, which are usually limited and do not guarantee the optimal selection of parameters. This paper presents a procedure for the optimization of the training dataset and the optimization of the structural parameters of a neural network through the application of a robust neural network design methodology based on the design philosophy proposed by Genichi Taguchi, applied to the solution of inverse kinematics in an open source, six-degrees-of-freedom robotic manipulator. The results obtained during the optimization process of the structural parameters of the network show an improvement in the accuracy of the results, reaching a high prediction percentage and maintaining a margin of error of less than 5%.
Boosting by the world’s context, several efforts have been maiden toward the digital transformation of Education. Technology-based active learning has become pivotal in pursuing a more flexible education system. This work presents the development of an Educational Mechatronics (EM) training system based on computer vision that performs as a positioning system for mobile robots in the 2D plane. The results show that the obtained precision, exactitude, and resolution of the EM training system are suitable for robotics applications comprising position, velocity, and acceleration variables. Moreover, an instructional design aligned with the EM conceptual framework, using the EM training system and a LEGO mobile robot, is presented to construct the mechatronic concept: line segment.
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