- author = "Zlatan Car and Sandi {Baressi Segota} and Nikola Andelic and Ivan Lorencin and Jelena Musulin and Daniel Stifanic and Vedran Mrzljak",
- title = "Determining inverse kinematics of a serial robotic manipulator through the use of genetic programming algorithm",
- booktitle = "18th International Congress of Serbian Society of Mechanics",
- year = "2021",
- editor = "Nenad Filipovic and Milos Kojic",
- address = "Kragujevac, Serbia",
- month = jun # " 28-30",
- keywords = "genetic algorithms, genetic programming, industrial robotics, inverse kinematics",
-
URL = "
https://www.bib.irb.hr:8443/1133808/download/1133808.Contribution_template_2021.edited.pdf",
- size = "6 pages",
- abstract = "Inverse kinematics is one of the key parts of any industrial robotic manipulator modeling.Solving the inverse kinematics of a robotic manipulator in the classical analytical manner is fairly complex and error-prone. While previous research has shown the possibility of AI application for inverse kinematics solutions, such models have certain pitfalls which makes the probability of their use low. For this reason, the authors propose Genetic Programming (GP), which is an AI method that generates models in the shapes of equations. The application of the algorithm in question shows promise, with errors being lower than 0.5 degrees for all regressed joints when evaluated using Mean Absolute Error (MAE). This points towards the fact that GP could be used in such an approach but some details of the algorithm need to be addressed, such as the tendency to generate large and simplifiable equations, or lower precision when compared to previous AI-based solutions to the same problem.",
-
notes = "http://www.ssm.kg.ac.rs/congress_2021/
Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia",
