A Methodology to Characterize an Optimal Robotic Manipulator Using PSO and ML Algorithms for Selective and Site-Specific Spraying Tasks in Vineyards
This paper presents an improved methodology for characterizing task-oriented optimal manipulator configuration, tested on a case study of selective spraying in vineyards. It compares the current approach for optimizing manipulator configurations, which relies on simulation and optimization algorithm...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Robotics |
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| Online Access: | https://www.mdpi.com/2218-6581/14/5/58 |
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| author | Roni Azriel Oded Degani Avital Bechar |
| author_facet | Roni Azriel Oded Degani Avital Bechar |
| author_sort | Roni Azriel |
| collection | DOAJ |
| description | This paper presents an improved methodology for characterizing task-oriented optimal manipulator configuration, tested on a case study of selective spraying in vineyards. It compares the current approach for optimizing manipulator configurations, which relies on simulation and optimization algorithms, with an improved methodology that integrates machine learning models to enhance the optimization process. The simulation tool was developed using the Gazebo simulator and ROS software to evaluate potential robotic configurations within a simulated vineyard. Particle Swarm Optimization (PSO) was employed as the optimization algorithm in a finite solution space, with the performance measure based on maximizing the Manipulability Index of manipulator configurations reaching all targets. In the proposed methodology, XGBoost models were used to replace the simulation stage in the process and predict the manipulator’s ability to reach the target positions in the spraying task. This prediction served as decision support in selecting which configurations should be tested in the simulation, thereby reducing computational time. The integration of machine learning models in the proposed methodology resulted in an average runtime reduction of 59% while maintaining an average manipulability index score in comparison to the original approach, which did not include the XGBoost model. This methodology demonstrates significant enhancements in optimizing robot configuration for a specific task and shows strong potential for broader applications across various industries. |
| format | Article |
| id | doaj-art-d1d4c4e2a00c44af9af225d385265d7e |
| institution | Kabale University |
| issn | 2218-6581 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Robotics |
| spelling | doaj-art-d1d4c4e2a00c44af9af225d385265d7e2025-08-20T03:48:01ZengMDPI AGRobotics2218-65812025-04-011455810.3390/robotics14050058A Methodology to Characterize an Optimal Robotic Manipulator Using PSO and ML Algorithms for Selective and Site-Specific Spraying Tasks in VineyardsRoni Azriel0Oded Degani1Avital Bechar2Institute of Agricultural and Biosystems Engineering, Agricultural Research Organization, Volcani Institute, Rishon LeZion 7505101, IsraelInstitute of Plant Sciences, Agricultural Research Organization, Volcani Institute, Rishon LeZion 7505101, IsraelInstitute of Agricultural and Biosystems Engineering, Agricultural Research Organization, Volcani Institute, Rishon LeZion 7505101, IsraelThis paper presents an improved methodology for characterizing task-oriented optimal manipulator configuration, tested on a case study of selective spraying in vineyards. It compares the current approach for optimizing manipulator configurations, which relies on simulation and optimization algorithms, with an improved methodology that integrates machine learning models to enhance the optimization process. The simulation tool was developed using the Gazebo simulator and ROS software to evaluate potential robotic configurations within a simulated vineyard. Particle Swarm Optimization (PSO) was employed as the optimization algorithm in a finite solution space, with the performance measure based on maximizing the Manipulability Index of manipulator configurations reaching all targets. In the proposed methodology, XGBoost models were used to replace the simulation stage in the process and predict the manipulator’s ability to reach the target positions in the spraying task. This prediction served as decision support in selecting which configurations should be tested in the simulation, thereby reducing computational time. The integration of machine learning models in the proposed methodology resulted in an average runtime reduction of 59% while maintaining an average manipulability index score in comparison to the original approach, which did not include the XGBoost model. This methodology demonstrates significant enhancements in optimizing robot configuration for a specific task and shows strong potential for broader applications across various industries.https://www.mdpi.com/2218-6581/14/5/58robotic manipulatoroptimal designsimulationparticle swarm optimizationmachine learningspraying task |
| spellingShingle | Roni Azriel Oded Degani Avital Bechar A Methodology to Characterize an Optimal Robotic Manipulator Using PSO and ML Algorithms for Selective and Site-Specific Spraying Tasks in Vineyards Robotics robotic manipulator optimal design simulation particle swarm optimization machine learning spraying task |
| title | A Methodology to Characterize an Optimal Robotic Manipulator Using PSO and ML Algorithms for Selective and Site-Specific Spraying Tasks in Vineyards |
| title_full | A Methodology to Characterize an Optimal Robotic Manipulator Using PSO and ML Algorithms for Selective and Site-Specific Spraying Tasks in Vineyards |
| title_fullStr | A Methodology to Characterize an Optimal Robotic Manipulator Using PSO and ML Algorithms for Selective and Site-Specific Spraying Tasks in Vineyards |
| title_full_unstemmed | A Methodology to Characterize an Optimal Robotic Manipulator Using PSO and ML Algorithms for Selective and Site-Specific Spraying Tasks in Vineyards |
| title_short | A Methodology to Characterize an Optimal Robotic Manipulator Using PSO and ML Algorithms for Selective and Site-Specific Spraying Tasks in Vineyards |
| title_sort | methodology to characterize an optimal robotic manipulator using pso and ml algorithms for selective and site specific spraying tasks in vineyards |
| topic | robotic manipulator optimal design simulation particle swarm optimization machine learning spraying task |
| url | https://www.mdpi.com/2218-6581/14/5/58 |
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