Adaptive SOM-GA Hybrid Algorithm for Grasping Sequence Optimization in Apple Harvesting Robots: Enhancing Efficiency in Open-Field Orchards
To address the challenge of low operational efficiency in apple harvesting robots, this study proposes an adaptive grasping sequence planning methodology that combines Self-Organizing Maps (SOMs) and genetic algorithms (GAs). The proposed adaptive SOM—GA hybrid algorithm aims to minimize cycle time...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/5/1230 |
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| author | Li Zhang Zhihui He Haobin Zhu Zhanhong Wei Juan Lu Xiongkui He |
| author_facet | Li Zhang Zhihui He Haobin Zhu Zhanhong Wei Juan Lu Xiongkui He |
| author_sort | Li Zhang |
| collection | DOAJ |
| description | To address the challenge of low operational efficiency in apple harvesting robots, this study proposes an adaptive grasping sequence planning methodology that combines Self-Organizing Maps (SOMs) and genetic algorithms (GAs). The proposed adaptive SOM—GA hybrid algorithm aims to minimize cycle time by optimizing the path planning between the fruit detection and grasping phases. First of all, we propose a density-aware adaptive mechanism that dynamically adjusts planning strategies based on fruit count thresholds. In addition, the proposed grasping sequence planning framework for high-density dwarf cultivation (HDDC) orchards is validated through threshold sensitivity analysis and empirical analysis of over 500 real-world fruit distribution samples. Finally, comparative experiments demonstrate that our proposed method reduces path length in high-density scenarios. Statistical analysis reveals a bimodal fruit distribution, which aligns the algorithm’s adaptive thresholds with real-world operational demands. These advancements improve theoretical research and enhance the commercial viability in agricultural robotics. |
| format | Article |
| id | doaj-art-8367ef5132d8416699f4dd76d06a5df4 |
| institution | OA Journals |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-8367ef5132d8416699f4dd76d06a5df42025-08-20T01:57:01ZengMDPI AGAgronomy2073-43952025-05-01155123010.3390/agronomy15051230Adaptive SOM-GA Hybrid Algorithm for Grasping Sequence Optimization in Apple Harvesting Robots: Enhancing Efficiency in Open-Field OrchardsLi Zhang0Zhihui He1Haobin Zhu2Zhanhong Wei3Juan Lu4Xiongkui He5College of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, ChinaRobotics Institute, Ningbo University of Technology, Ningbo 315211, ChinaCollege of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, ChinaCollege of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, ChinaCollege of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, ChinaCollege of Agricultural Unmanned Systems, China Agricultural University, Beijing 100193, ChinaTo address the challenge of low operational efficiency in apple harvesting robots, this study proposes an adaptive grasping sequence planning methodology that combines Self-Organizing Maps (SOMs) and genetic algorithms (GAs). The proposed adaptive SOM—GA hybrid algorithm aims to minimize cycle time by optimizing the path planning between the fruit detection and grasping phases. First of all, we propose a density-aware adaptive mechanism that dynamically adjusts planning strategies based on fruit count thresholds. In addition, the proposed grasping sequence planning framework for high-density dwarf cultivation (HDDC) orchards is validated through threshold sensitivity analysis and empirical analysis of over 500 real-world fruit distribution samples. Finally, comparative experiments demonstrate that our proposed method reduces path length in high-density scenarios. Statistical analysis reveals a bimodal fruit distribution, which aligns the algorithm’s adaptive thresholds with real-world operational demands. These advancements improve theoretical research and enhance the commercial viability in agricultural robotics.https://www.mdpi.com/2073-4395/15/5/1230design harvesting robotsgrasping sequence plantraveling salesman problem |
| spellingShingle | Li Zhang Zhihui He Haobin Zhu Zhanhong Wei Juan Lu Xiongkui He Adaptive SOM-GA Hybrid Algorithm for Grasping Sequence Optimization in Apple Harvesting Robots: Enhancing Efficiency in Open-Field Orchards Agronomy design harvesting robots grasping sequence plan traveling salesman problem |
| title | Adaptive SOM-GA Hybrid Algorithm for Grasping Sequence Optimization in Apple Harvesting Robots: Enhancing Efficiency in Open-Field Orchards |
| title_full | Adaptive SOM-GA Hybrid Algorithm for Grasping Sequence Optimization in Apple Harvesting Robots: Enhancing Efficiency in Open-Field Orchards |
| title_fullStr | Adaptive SOM-GA Hybrid Algorithm for Grasping Sequence Optimization in Apple Harvesting Robots: Enhancing Efficiency in Open-Field Orchards |
| title_full_unstemmed | Adaptive SOM-GA Hybrid Algorithm for Grasping Sequence Optimization in Apple Harvesting Robots: Enhancing Efficiency in Open-Field Orchards |
| title_short | Adaptive SOM-GA Hybrid Algorithm for Grasping Sequence Optimization in Apple Harvesting Robots: Enhancing Efficiency in Open-Field Orchards |
| title_sort | adaptive som ga hybrid algorithm for grasping sequence optimization in apple harvesting robots enhancing efficiency in open field orchards |
| topic | design harvesting robots grasping sequence plan traveling salesman problem |
| url | https://www.mdpi.com/2073-4395/15/5/1230 |
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