MB-RRT: An Inverse Kinematics Solver of Reduced Dimension
The evolution of manipulator robots has increased the complexity of their models and applications, requiring that the inverse kinematics (IK) methods integrated into their control systems to have features such as fast convergence, completeness, low computational cost, and the ability to avoid local...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9591603/ |
| _version_ | 1818932490300030976 |
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| author | Matheus C. Santos Lucas Molina Elyson A. N. Carvalho Eduardo O. Freire Jose G. N. Carvalho Phillipe C. Santos |
| author_facet | Matheus C. Santos Lucas Molina Elyson A. N. Carvalho Eduardo O. Freire Jose G. N. Carvalho Phillipe C. Santos |
| author_sort | Matheus C. Santos |
| collection | DOAJ |
| description | The evolution of manipulator robots has increased the complexity of their models and applications, requiring that the inverse kinematics (IK) methods integrated into their control systems to have features such as fast convergence, completeness, low computational cost, and the ability to avoid local minima and singularities. We propose in this paper a new probabilistic IK solver based on the probabilistic search method known as Rapidly-Exploring Random Tree (RRT), the Workspace-RRT. The technique grows the tree as a spatial representation of the manipulator on the workspace instead of the configuration space, which reduces the search space up to 3 dimensions. Based on this new representation we also present the Manipulator-Based Rapidly Random Tree (MB-RRT) by incorporating to the Workspace-RRT a new probability model and a new metric for the closest node. We evaluate the presented methods through simulated experiments in the Matlab software. First, we evaluate the impact of the proposed aspects through a comparison between the RRT-based IK solvers, which emphasizes the proposed changes as a key to make the method suitable for the IK problem. At last, we show the use of the MB-RRT for precision tasks and obstructed environments. |
| first_indexed | 2024-12-20T04:33:19Z |
| format | Article |
| id | doaj.art-3784e9e8edb64ed98b11fd7c02c69ad2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T04:33:19Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3784e9e8edb64ed98b11fd7c02c69ad22022-12-21T19:53:19ZengIEEEIEEE Access2169-35362021-01-01914855814857310.1109/ACCESS.2021.31236459591603MB-RRT: An Inverse Kinematics Solver of Reduced DimensionMatheus C. Santos0https://orcid.org/0000-0002-0246-9297Lucas Molina1https://orcid.org/0000-0002-0417-402XElyson A. N. Carvalho2https://orcid.org/0000-0001-7903-426XEduardo O. Freire3https://orcid.org/0000-0002-0357-8655Jose G. N. Carvalho4Phillipe C. Santos5https://orcid.org/0000-0003-1378-8282Department of Electronic and Computer Engineering, University of Limerick, Limerick, IrelandDepartment of Electrical Engineering, Federal University of Sergipe, São Cristóvão, BrazilDepartment of Electrical Engineering, Federal University of Sergipe, São Cristóvão, BrazilDepartment of Electrical Engineering, Federal University of Sergipe, São Cristóvão, BrazilDepartment of Electrical Engineering, Federal University of Sergipe, São Cristóvão, BrazilDepartment of Electrical Engineering, Federal University of Campina Grande, Campina Grande, BrazilThe evolution of manipulator robots has increased the complexity of their models and applications, requiring that the inverse kinematics (IK) methods integrated into their control systems to have features such as fast convergence, completeness, low computational cost, and the ability to avoid local minima and singularities. We propose in this paper a new probabilistic IK solver based on the probabilistic search method known as Rapidly-Exploring Random Tree (RRT), the Workspace-RRT. The technique grows the tree as a spatial representation of the manipulator on the workspace instead of the configuration space, which reduces the search space up to 3 dimensions. Based on this new representation we also present the Manipulator-Based Rapidly Random Tree (MB-RRT) by incorporating to the Workspace-RRT a new probability model and a new metric for the closest node. We evaluate the presented methods through simulated experiments in the Matlab software. First, we evaluate the impact of the proposed aspects through a comparison between the RRT-based IK solvers, which emphasizes the proposed changes as a key to make the method suitable for the IK problem. At last, we show the use of the MB-RRT for precision tasks and obstructed environments.https://ieeexplore.ieee.org/document/9591603/Inverse kinematicsmanipulatorsrobotsRRT |
| spellingShingle | Matheus C. Santos Lucas Molina Elyson A. N. Carvalho Eduardo O. Freire Jose G. N. Carvalho Phillipe C. Santos MB-RRT: An Inverse Kinematics Solver of Reduced Dimension IEEE Access Inverse kinematics manipulators robots RRT |
| title | MB-RRT: An Inverse Kinematics Solver of Reduced Dimension |
| title_full | MB-RRT: An Inverse Kinematics Solver of Reduced Dimension |
| title_fullStr | MB-RRT: An Inverse Kinematics Solver of Reduced Dimension |
| title_full_unstemmed | MB-RRT: An Inverse Kinematics Solver of Reduced Dimension |
| title_short | MB-RRT: An Inverse Kinematics Solver of Reduced Dimension |
| title_sort | mb rrt an inverse kinematics solver of reduced dimension |
| topic | Inverse kinematics manipulators robots RRT |
| url | https://ieeexplore.ieee.org/document/9591603/ |
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