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Robotic-Arm-Based Force Control by Deep Deterministic Policy Gradient in Neurosurgical Practice

Author

Listed:
  • Ibai Inziarte-Hidalgo

    (Research & Development Department, Montajes Mantenimiento y Automatismos Electricos Navarra S.L., 01010 Vitoria-Gasteiz, Spain
    Automatic Control and System Engineering Department, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain)

  • Erik Gorospe

    (Automatic Control and System Engineering Department, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain)

  • Ekaitz Zulueta

    (Department of Nuclear and Fluid Mechanics, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain)

  • Jose Manuel Lopez-Guede

    (Department of Mechanical Engineering, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain)

  • Unai Fernandez-Gamiz

    (Department of Nuclear Engineering and Fluid Mechanics, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain)

  • Saioa Etxebarria

    (Department of Mechanical Engineering, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain)

Abstract

This research continues the previous work “Robotic-Arm-Based Force Control in Neurosurgical Practice”. In that study, authors acquired an optimal control arm speed shape for neurological surgery which minimized a cost function that uses an adaptive scheme to determine the brain tissue force. At the end, the authors proposed the use of reinforcement learning, more specifically Deep Deterministic Policy Gradient (DDPG), to create an agent that could obtain the optimal solution through self-training. In this article, that proposal is carried out by creating an environment, agent (actor and critic), and reward function, that obtain a solution for our problem. However, we have drawn conclusions for potential future enhancements. Additionally, we analyzed the results and identified mistakes that can be improved upon in the future, such as exploring the use of varying desired distances of retraction to enhance training.

Suggested Citation

  • Ibai Inziarte-Hidalgo & Erik Gorospe & Ekaitz Zulueta & Jose Manuel Lopez-Guede & Unai Fernandez-Gamiz & Saioa Etxebarria, 2023. "Robotic-Arm-Based Force Control by Deep Deterministic Policy Gradient in Neurosurgical Practice," Mathematics, MDPI, vol. 11(19), pages 1-19, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:19:p:4133-:d:1251457
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    References listed on IDEAS

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    1. Ibai Inziarte-Hidalgo & Irantzu Uriarte & Unai Fernandez-Gamiz & Gorka Sorrosal & Ekaitz Zulueta, 2023. "Robotic-Arm-Based Force Control in Neurosurgical Practice," Mathematics, MDPI, vol. 11(4), pages 1-12, February.
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