Mapping Robot Singularities through the Monte Carlo Method
In addition to other things, a robot’s design also determines its singularity configurations and points in the workspace. In designing the robot’s working trajectory, one of the main issues of robot steering is avoiding singularities. The article proposes a different approach to calculating the inve...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/16/8330 |
| _version_ | 1797411326425825280 |
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| author | Tomáš Stejskal Jozef Svetlík Štefan Ondočko |
| author_facet | Tomáš Stejskal Jozef Svetlík Štefan Ondočko |
| author_sort | Tomáš Stejskal |
| collection | DOAJ |
| description | In addition to other things, a robot’s design also determines its singularity configurations and points in the workspace. In designing the robot’s working trajectory, one of the main issues of robot steering is avoiding singularities. The article proposes a different approach to calculating the inverse task, which lies in the random mapping of the robot mechanism’s workspace through searching for points closest in proximity to the trajectory in question. The new methodology of mapping and detecting the states of singularity in the workspace is actually based on Monte Carlo analysis, since we were also interested in the number of occurrences. In terms of mathematical analysis, this method is less demanding, because in searching for joint variables suitable for the given trajectory, it does not use inverse calculation. It is important that the method is chosen appropriately. The method is sufficiently illustrative in the form of a graph, making, e.g., programming optimization simpler. The ultimate effect is the reduced time needed for computing joint variables and the availability of an option to select a robot configuration suitable for carrying out the required work. The paper offers an example of an analysis concerning three different robots. |
| first_indexed | 2024-03-09T04:43:29Z |
| format | Article |
| id | doaj.art-f01f99f9887842e08864ffcd9188bcad |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T04:43:29Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f01f99f9887842e08864ffcd9188bcad2023-12-03T13:18:31ZengMDPI AGApplied Sciences2076-34172022-08-011216833010.3390/app12168330Mapping Robot Singularities through the Monte Carlo MethodTomáš Stejskal0Jozef Svetlík1Štefan Ondočko2Department of Production Systems and Robotics, Faculty of Mechanical Engineering, Technical University of Košice, Letná 9, 04001 Košice, SlovakiaDepartment of Production Systems and Robotics, Faculty of Mechanical Engineering, Technical University of Košice, Letná 9, 04001 Košice, SlovakiaDepartment of Production Systems and Robotics, Faculty of Mechanical Engineering, Technical University of Košice, Letná 9, 04001 Košice, SlovakiaIn addition to other things, a robot’s design also determines its singularity configurations and points in the workspace. In designing the robot’s working trajectory, one of the main issues of robot steering is avoiding singularities. The article proposes a different approach to calculating the inverse task, which lies in the random mapping of the robot mechanism’s workspace through searching for points closest in proximity to the trajectory in question. The new methodology of mapping and detecting the states of singularity in the workspace is actually based on Monte Carlo analysis, since we were also interested in the number of occurrences. In terms of mathematical analysis, this method is less demanding, because in searching for joint variables suitable for the given trajectory, it does not use inverse calculation. It is important that the method is chosen appropriately. The method is sufficiently illustrative in the form of a graph, making, e.g., programming optimization simpler. The ultimate effect is the reduced time needed for computing joint variables and the availability of an option to select a robot configuration suitable for carrying out the required work. The paper offers an example of an analysis concerning three different robots.https://www.mdpi.com/2076-3417/12/16/8330robot singularityMonte Carlo simulationJacobian determinantMatlab |
| spellingShingle | Tomáš Stejskal Jozef Svetlík Štefan Ondočko Mapping Robot Singularities through the Monte Carlo Method Applied Sciences robot singularity Monte Carlo simulation Jacobian determinant Matlab |
| title | Mapping Robot Singularities through the Monte Carlo Method |
| title_full | Mapping Robot Singularities through the Monte Carlo Method |
| title_fullStr | Mapping Robot Singularities through the Monte Carlo Method |
| title_full_unstemmed | Mapping Robot Singularities through the Monte Carlo Method |
| title_short | Mapping Robot Singularities through the Monte Carlo Method |
| title_sort | mapping robot singularities through the monte carlo method |
| topic | robot singularity Monte Carlo simulation Jacobian determinant Matlab |
| url | https://www.mdpi.com/2076-3417/12/16/8330 |
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