Optimizing parameters of robotic task-oriented programming via a multiphysics simulation

The programming complexity of industrial robots significantly limits their expansion in complex industrial applications. Consequently, research has focused extensively on the development of intuitive programming methods.This article proposes a framework for task-oriented programming introducing an i...

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Veröffentlicht in:Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation) S. 1 - 4
Hauptverfasser: Delledonne, Michele, Villagrossi, Enrico, Beschi, Manuel
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 12.09.2023
Schlagworte:
Bayes methods
Complexity theory
Genetics
Industrial robots
Intuitive robot programming
Optimization
Robotic tasks optimization
Task analysis
Task-oriented programming
Virtual environments
ISSN:1946-0759
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Zusammenfassung:The programming complexity of industrial robots significantly limits their expansion in complex industrial applications. Consequently, research has focused extensively on the development of intuitive programming methods.This article proposes a framework for task-oriented programming introducing an intuitive and modular task structure. The framework provides an algorithm able to optimize the execution parameter of the tasks. A physical simulation environment allows accurate parameter optimization in a virtual environment providing feasible and safe results. Efficiency tests demonstrated the method's effectiveness, and a comparison with genetic and Bayesian -based ones have been conducted.
ISSN:1946-0759
DOI:10.1109/ETFA54631.2023.10275720