Workpiece Placement Optimization for Robot Machining Based on the Evaluation of Feasible Kinematic Directional Capabilities
Workpiece placement plays a crucial role when performing complex surface machining task robotically. If the feasibility of a robotic task needs to be guaranteed, the maximum available capabilities should be higher than the joint capabilities required for task execution. This can be challenging, espe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/4/1531 |
| _version_ | 1827344222158585856 |
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| author | Saša Stradovnik Aleš Hace |
| author_facet | Saša Stradovnik Aleš Hace |
| author_sort | Saša Stradovnik |
| collection | DOAJ |
| description | Workpiece placement plays a crucial role when performing complex surface machining task robotically. If the feasibility of a robotic task needs to be guaranteed, the maximum available capabilities should be higher than the joint capabilities required for task execution. This can be challenging, especially when performing a complex surface machining task with a collaborative robot, which tend to have lower motion capabilities than conventional industrial robots. Therefore, the kinematic and dynamic capabilities within the robot workspace should be evaluated prior to task execution and optimized considering specific task requirements. In order to estimate maximum directional kinematic capabilities considering the requirements of the surface machining task in a physically consistent and accurate way, the Decomposed Twist Feasibility (DTF) method will be used in this paper. Estimation of the total kinematic performance capabilities can be determined accurately and simply using this method, adjusted specifically for robotic surface machining purposes. In this study, we present the numerical results that prove the effectiveness of the DTF method in identifying the optimal placement of predetermined machining tasks within the robot’s workspace that requires lowest possible joint velocities for task execution. These findings highlight the practicality of the DTF method in enhancing the feasibility of complex robotic surface machining operations. |
| first_indexed | 2024-03-07T22:43:54Z |
| format | Article |
| id | doaj.art-c2d12d6219ed49f7b63e304b32b2864c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-07T22:43:54Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c2d12d6219ed49f7b63e304b32b2864c2024-02-23T15:06:18ZengMDPI AGApplied Sciences2076-34172024-02-01144153110.3390/app14041531Workpiece Placement Optimization for Robot Machining Based on the Evaluation of Feasible Kinematic Directional CapabilitiesSaša Stradovnik0Aleš Hace1Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška Cesta 46, SI-2000 Maribor, SloveniaFaculty of Electrical Engineering and Computer Science, University of Maribor, Koroška Cesta 46, SI-2000 Maribor, SloveniaWorkpiece placement plays a crucial role when performing complex surface machining task robotically. If the feasibility of a robotic task needs to be guaranteed, the maximum available capabilities should be higher than the joint capabilities required for task execution. This can be challenging, especially when performing a complex surface machining task with a collaborative robot, which tend to have lower motion capabilities than conventional industrial robots. Therefore, the kinematic and dynamic capabilities within the robot workspace should be evaluated prior to task execution and optimized considering specific task requirements. In order to estimate maximum directional kinematic capabilities considering the requirements of the surface machining task in a physically consistent and accurate way, the Decomposed Twist Feasibility (DTF) method will be used in this paper. Estimation of the total kinematic performance capabilities can be determined accurately and simply using this method, adjusted specifically for robotic surface machining purposes. In this study, we present the numerical results that prove the effectiveness of the DTF method in identifying the optimal placement of predetermined machining tasks within the robot’s workspace that requires lowest possible joint velocities for task execution. These findings highlight the practicality of the DTF method in enhancing the feasibility of complex robotic surface machining operations.https://www.mdpi.com/2076-3417/14/4/1531workpiece placement optimizationrobotic surface machiningfeasible kinematic directional capabilitiesdecomposed twist feasibility (DTF) methodmanipulabilitynon-linear optimization |
| spellingShingle | Saša Stradovnik Aleš Hace Workpiece Placement Optimization for Robot Machining Based on the Evaluation of Feasible Kinematic Directional Capabilities Applied Sciences workpiece placement optimization robotic surface machining feasible kinematic directional capabilities decomposed twist feasibility (DTF) method manipulability non-linear optimization |
| title | Workpiece Placement Optimization for Robot Machining Based on the Evaluation of Feasible Kinematic Directional Capabilities |
| title_full | Workpiece Placement Optimization for Robot Machining Based on the Evaluation of Feasible Kinematic Directional Capabilities |
| title_fullStr | Workpiece Placement Optimization for Robot Machining Based on the Evaluation of Feasible Kinematic Directional Capabilities |
| title_full_unstemmed | Workpiece Placement Optimization for Robot Machining Based on the Evaluation of Feasible Kinematic Directional Capabilities |
| title_short | Workpiece Placement Optimization for Robot Machining Based on the Evaluation of Feasible Kinematic Directional Capabilities |
| title_sort | workpiece placement optimization for robot machining based on the evaluation of feasible kinematic directional capabilities |
| topic | workpiece placement optimization robotic surface machining feasible kinematic directional capabilities decomposed twist feasibility (DTF) method manipulability non-linear optimization |
| url | https://www.mdpi.com/2076-3417/14/4/1531 |
| work_keys_str_mv | AT sasastradovnik workpieceplacementoptimizationforrobotmachiningbasedontheevaluationoffeasiblekinematicdirectionalcapabilities AT aleshace workpieceplacementoptimizationforrobotmachiningbasedontheevaluationoffeasiblekinematicdirectionalcapabilities |