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Algorithms of Posteriori Multi-objective Optimization for Robotic Gripper Design

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Interactive Collaborative Robotics (ICR 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12336))

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Abstract

In this paper, the posteriori methods, such as NSGA-II, MOGWO and MOPSO, are considered in solving multicriteria optimization problems. The main goal of these methods is to find set of non-dominated solutions or Pareto front using Pareto dominance. A kinematic description of the processed four-finger gripper prototype for picking tomatoes, its objective functions and constraint functions are presented. The main advantage of this prototype is that it uses the same driver to simultaneously control the movements of the fingers and the suction nozzle. Three optimization cases are considered: optimization of two objective functions, optimization of objective functions, and optimization of all objective functions simultaneously. To optimize the task of multi-objective functions, a normalized weighted objective function with weightage factors is used. The results of optimizing the kinematic gripper design using posteriori methods method performance measure are presented. Depending on the selected objective functions, we can choose a set of sizes of kinematic gripper elements.

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Acknowledgments

The presented work was supported by the Russian Science Foundation (grant No. 16-19-00044П).

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Authors and Affiliations

  1. Department of Electromechanics and Robotics, SUAI, Bolshaya Morskaya Street, 67, 190000, St. Petersburg, Russia

    Quyen Vu

  2. Laboratory of Autonomous Robotic Systems, St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 14-th line of V.I., 39, 199178, St. Petersburg, Russia

    Andrey Ronzhin

Authors
  1. Quyen Vu
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  2. Andrey Ronzhin
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Correspondence to Andrey Ronzhin .

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Editors and Affiliations

  1. St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg, Russia

    Andrey Ronzhin

  2. Technical University of Munich, Munich, Germany

    Gerhard Rigoll

  3. Institute of Control Sciences of the Russian Academy of Sciences, Moscow, Russia

    Roman Meshcheryakov

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Vu, Q., Ronzhin, A. (2020). Algorithms of Posteriori Multi-objective Optimization for Robotic Gripper Design. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds) Interactive Collaborative Robotics. ICR 2020. Lecture Notes in Computer Science(), vol 12336. Springer, Cham. https://doi.org/10.1007/978-3-030-60337-3_30

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  • DOI: https://doi.org/10.1007/978-3-030-60337-3_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60336-6

  • Online ISBN: 978-3-030-60337-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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