Robotic-Arm-Based Force Control by Deep Deterministic Policy Gradient in Neurosurgical Practice
dc.contributor.author | Inziarte Hidalgo, Ibai | |
dc.contributor.author | Gorospe, Erik | |
dc.contributor.author | Zulueta Guerrero, Ekaitz | |
dc.contributor.author | López Guede, José Manuel | |
dc.contributor.author | Fernández Gámiz, Unai | |
dc.contributor.author | Etxebarria Berrizbeitia, Saioa | |
dc.date.accessioned | 2023-10-16T17:43:22Z | |
dc.date.available | 2023-10-16T17:43:22Z | |
dc.date.issued | 2023-09-30 | |
dc.identifier.citation | Mathematics 11(19) : (2023) // Article ID 4133 | es_ES |
dc.identifier.issn | 2227-7390 | |
dc.identifier.uri | http://hdl.handle.net/10810/62852 | |
dc.description.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. | es_ES |
dc.description.sponsorship | The authors were supported by the government of the Basque Country through the research grant ELKARTEK KK-2023/00058 DEEPBASK (Creación de nuevos algoritmos de aprendizaje profundo aplicado a la industria). This study has also been conducted partially under the framework of the project ADA (Grants for R&D projects 2022 and supported by the European Regional Development Funds). | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.subject | neurosurgical robotics | es_ES |
dc.subject | optimal control | es_ES |
dc.subject | reinforcement learning | es_ES |
dc.subject | deep deterministic policy gradient | es_ES |
dc.title | Robotic-Arm-Based Force Control by Deep Deterministic Policy Gradient in Neurosurgical Practice | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2023-10-13T12:07:48Z | |
dc.rights.holder | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/2227-7390/11/19/4133 | es_ES |
dc.identifier.doi | 10.3390/math11194133 | |
dc.departamentoes | Ingeniería de sistemas y automática | |
dc.departamentoes | Ingeniería mecánica | |
dc.departamentoes | Ingeniería nuclear y mecánica de fluidos | |
dc.departamentoeu | Ingeniaritza nuklearra eta jariakinen mekanika | |
dc.departamentoeu | Sistemen ingeniaritza eta automatika | |
dc.departamentoeu | Ingeniaritza mekanikoa |
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Except where otherwise noted, this item's license is described as © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).