Inverse Kinematics for Robotic Manipulators via Deep Neural Networks: Experiments and Results
This paper explores the application of Deep Neural Networks (DNNs) to solve the Inverse Kinematics (IK) problem in robotic manipulators. The IK problem, crucial for ensuring precision in robotic movements, involves determining joint configurations for a manipulator to reach a desired position or ori...
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MDPI AG
2025-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/13/7226 |
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| author | Ana Calzada-Garcia Juan G. Victores Francisco J. Naranjo-Campos Carlos Balaguer |
| author_facet | Ana Calzada-Garcia Juan G. Victores Francisco J. Naranjo-Campos Carlos Balaguer |
| author_sort | Ana Calzada-Garcia |
| collection | DOAJ |
| description | This paper explores the application of Deep Neural Networks (DNNs) to solve the Inverse Kinematics (IK) problem in robotic manipulators. The IK problem, crucial for ensuring precision in robotic movements, involves determining joint configurations for a manipulator to reach a desired position or orientation. Traditional methods, such as analytical and numerical approaches, have limitations, especially for redundant manipulators, or involve high computational costs. Recent advances in machine learning, particularly with DNNs, have shown promising results and seem fit for addressing these challenges. This study investigates several DNN architectures, namely Feed-Forward Multilayer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), for solving the IK problem, using the TIAGo robotic arm with seven Degrees of Freedom (DOFs). Different training datasets, normalization techniques, and orientation representations are tested, and custom metrics are introduced to evaluate position and orientation errors. The performance of these models is compared, with a focus on curriculum learning to optimize training. The results demonstrate the potential of DNNs to efficiently solve the IK problem while avoiding issues such as singularities, competing with traditional methods in precision and speed. |
| format | Article |
| id | doaj-art-ef578ec01c0846da87bb42ee17cbdd2a |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-ef578ec01c0846da87bb42ee17cbdd2a2025-08-20T03:28:36ZengMDPI AGApplied Sciences2076-34172025-06-011513722610.3390/app15137226Inverse Kinematics for Robotic Manipulators via Deep Neural Networks: Experiments and ResultsAna Calzada-Garcia0Juan G. Victores1Francisco J. Naranjo-Campos2Carlos Balaguer3RoboticsLab, Systems and Automation Engineering Department, University Carlos III of Madrid, 28911 Leganés, SpainRoboticsLab, Systems and Automation Engineering Department, University Carlos III of Madrid, 28911 Leganés, SpainRoboticsLab, Systems and Automation Engineering Department, University Carlos III of Madrid, 28911 Leganés, SpainRoboticsLab, Systems and Automation Engineering Department, University Carlos III of Madrid, 28911 Leganés, SpainThis paper explores the application of Deep Neural Networks (DNNs) to solve the Inverse Kinematics (IK) problem in robotic manipulators. The IK problem, crucial for ensuring precision in robotic movements, involves determining joint configurations for a manipulator to reach a desired position or orientation. Traditional methods, such as analytical and numerical approaches, have limitations, especially for redundant manipulators, or involve high computational costs. Recent advances in machine learning, particularly with DNNs, have shown promising results and seem fit for addressing these challenges. This study investigates several DNN architectures, namely Feed-Forward Multilayer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), for solving the IK problem, using the TIAGo robotic arm with seven Degrees of Freedom (DOFs). Different training datasets, normalization techniques, and orientation representations are tested, and custom metrics are introduced to evaluate position and orientation errors. The performance of these models is compared, with a focus on curriculum learning to optimize training. The results demonstrate the potential of DNNs to efficiently solve the IK problem while avoiding issues such as singularities, competing with traditional methods in precision and speed.https://www.mdpi.com/2076-3417/15/13/7226deep learninginverse kinematicsroboticscurriculum learning |
| spellingShingle | Ana Calzada-Garcia Juan G. Victores Francisco J. Naranjo-Campos Carlos Balaguer Inverse Kinematics for Robotic Manipulators via Deep Neural Networks: Experiments and Results Applied Sciences deep learning inverse kinematics robotics curriculum learning |
| title | Inverse Kinematics for Robotic Manipulators via Deep Neural Networks: Experiments and Results |
| title_full | Inverse Kinematics for Robotic Manipulators via Deep Neural Networks: Experiments and Results |
| title_fullStr | Inverse Kinematics for Robotic Manipulators via Deep Neural Networks: Experiments and Results |
| title_full_unstemmed | Inverse Kinematics for Robotic Manipulators via Deep Neural Networks: Experiments and Results |
| title_short | Inverse Kinematics for Robotic Manipulators via Deep Neural Networks: Experiments and Results |
| title_sort | inverse kinematics for robotic manipulators via deep neural networks experiments and results |
| topic | deep learning inverse kinematics robotics curriculum learning |
| url | https://www.mdpi.com/2076-3417/15/13/7226 |
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