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Didactic use of genetic algorithms: a model for teaching robotics
Author(s) -
José Tarcísio Franco de Camargo,
Eliana Anunciato Franco de Camargo,
Estéfano Vizconde Veraszto,
Gilmar Barreto,
Jorge Cândido
Publication year - 2020
Publication title -
revista brasileira de ensino de ciência e tecnologia
Language(s) - English
Resource type - Journals
ISSN - 1982-873X
DOI - 10.3895/rbect.v13n1.8218
Subject(s) - robotics , kinematics , representation (politics) , artificial intelligence , robot , inverse kinematics , computer science , genetic algorithm , inverse , control engineering , machine learning , mathematics , engineering , physics , geometry , classical mechanics , politics , political science , law
The study of articulated robots in higher education necessarily goes through the development of their kinematic models. The inverse kinematic model is usually described algebraically, although this representation is often difficult to obtain. Thus, the use of genetic algorithms in teaching robotics can be very attractive, since they allow students to easily develop models and predict the behavior of robots before their formal development. This way, the results of this work present a relatively fast way to simulate the inverse kinematic model, allowing the designer to have a broader view of the structure of a robot, coming to identify points that must be corrected or that can be optimized. It can be concluded that the use of genetic algorithms in robotics teaching is viable, having as main advantages their easy computational implementation and precision in the representation of kinematic models. .

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