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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Published in:Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation) pp. 1 - 4
Main Authors: Delledonne, Michele, Villagrossi, Enrico, Beschi, Manuel
Format: Conference Proceeding
Language:English
Published: IEEE 12.09.2023
Subjects:
Bayes methods
Complexity theory
Genetics
Industrial robots
Intuitive robot programming
Optimization
Robotic tasks optimization
Task analysis
Task-oriented programming
Virtual environments
ISSN:1946-0759
Online Access:Get full text
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Abstract 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.
AbstractList 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.
Author Delledonne, Michele
Beschi, Manuel
Villagrossi, Enrico
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  organization: University of Brescia,Department of Mechanical and Industrial Engineering,Brescia,Italy,25123
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  givenname: Enrico
  surname: Villagrossi
  fullname: Villagrossi, Enrico
  organization: National Research Council of Italy,Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing,Milan,Italy,20133
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  givenname: Manuel
  surname: Beschi
  fullname: Beschi, Manuel
  organization: University of Brescia,Department of Mechanical and Industrial Engineering,Brescia,Italy,25123
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Snippet The programming complexity of industrial robots significantly limits their expansion in complex industrial applications. Consequently, research has focused...
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SubjectTerms Bayes methods
Complexity theory
Genetics
Industrial robots
Intuitive robot programming
Optimization
Robotic tasks optimization
Task analysis
Task-oriented programming
Virtual environments
Title Optimizing parameters of robotic task-oriented programming via a multiphysics simulation
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