Design of Automatic Generation Platform for Agricultural Robot

The design of robots is highly dependent on their applications. For agricultural robots, terrain, weather, and crop diversity need to be considered, and work efficiency, cost, and reliability must be evaluated. These factors are important to determine the design of agricultural robots. In this study, we identified the constraint factors of agricultural robots from the perspectives of navigation, movement, control, cost, and reliability. The orthogonal defect classification (ODC) method was used to classify and grade these factors and explore the relationships among these factors. Based on the results, the design rules of agricultural robots were created, and an automatic production knowledge base of agricultural robot design was constructed. The results contribute to the automatic generation of the design framework of agricultural robots under specific environments to effectively improve the design level and quality of agricultural robots and popularize agricultural robots.

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Publication Details

Published
2025-08-04
DOI
https://doi.org/10.3390/engproc2025098045
Primary Topic
Advanced Manufacturing and Logistics Optimization
Type
article
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article

Design of Automatic Generation Platform for Agricultural Robot

Zhaowei Wang, Yurong Wang, Fangji Zhang
Advanced Manufacturing and Logistics Optimization
article

Design of Automatic Generation Platform for Agricultural Robot

Zhaowei Wang, Yurong Wang, Fangji Zhang
article en

Abstract

The design of robots is highly dependent on their applications. For agricultural robots, terrain, weather, and crop diversity need to be considered, and work efficiency, cost, and reliability must be evaluated. These factors are important to determine the design of agricultural robots. In this study, we identified the constraint factors of agricultural robots from the perspectives of navigation, movement, control, cost, and reliability. The orthogonal defect classification (ODC) method was used to classify and grade these factors and explore the relationships among these factors. Based on the results, the design rules of agricultural robots were created, and an automatic production knowledge base of agricultural robot design was constructed. The results contribute to the automatic generation of the design framework of agricultural robots under specific environments to effectively improve the design level and quality of agricultural robots and popularize agricultural robots.

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Advanced Manufacturing and Logistics Optimization
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