Field Complete Coverage Path Planning Based on Improved Genetic Algorithm for Transplanting Robot Article Swipe
YOU?
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· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/machines11060659
The Complete Coverage Path Planning (CCPP) is a key technology in the field of agricultural robots, and has great significance for improving the efficiency and quality of tillage, fertilization, harvesting, and other agricultural robot operations, as well as reducing the operation energy consumption. The traditional boustrophedon- or heuristic-search-algorithm-based CCPP methods, when coping with the field with irregular boundaries, obstacles, and other complex environments, still face many problems and challenges, such as large repeated work areas, multiple turns or U-turns, low operation efficiency, and prone to local optimum. In order to solve the above problems, an improved-genetic-algorithm-based CCPP method was proposed in this paper, the proposed method innovatively extends the traditional genetic algorithm’s chromosomes and single-point mutation into chromosome pairs and multi-point mutation, and proposed a multi-objective equilibrium fitness function. The simulation and experimental results on simple regular fields showed that the proposed improved-genetic-algorithm-based CCPP method achieved the comparable performance with the traditional boustrophedon-based CCPP method. However, on the complex irregular fields, the proposed CCPP method reduces 38.54% of repeated operation area and 35.00% of number of U-turns, and can save 7.82% of energy consumption on average. This proved that the proposed CCPP method has a strong adaptive capacity to the environment, and has practical application value in improving the efficiency and quality of agricultural machinery operations, and reducing the energy consumption.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/machines11060659
- https://www.mdpi.com/2075-1702/11/6/659/pdf?version=1687154354
- OA Status
- gold
- Cited By
- 14
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4381252311
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4381252311Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/machines11060659Digital Object Identifier
- Title
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Field Complete Coverage Path Planning Based on Improved Genetic Algorithm for Transplanting RobotWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-06-19Full publication date if available
- Authors
-
Xiuyin Wu, Jinqiang Bai, Fengqi Hao, Guanghe Cheng, Yongwei Tang, Xiuhua LiList of authors in order
- Landing page
-
https://doi.org/10.3390/machines11060659Publisher landing page
- PDF URL
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https://www.mdpi.com/2075-1702/11/6/659/pdf?version=1687154354Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2075-1702/11/6/659/pdf?version=1687154354Direct OA link when available
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Genetic algorithm, Mathematical optimization, Computer science, Energy consumption, Algorithm, Mathematics, Engineering, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
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14Total citation count in OpenAlex
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2025: 8, 2024: 4, 2023: 2Per-year citation counts (last 5 years)
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35Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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