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Tea Harvest Robot Navigation Path Generation Algorithm Based on Semantic Segmentation Using a Visual Sensor

文献类型： 外文期刊

作者： Tao, Houqi 1 ; Zhang, Ruirui 2 ; Zhang, Linhuan 1 ; Zhang, Danzhu 2 ; Yi, Tongchuan 2 ; Wu, Mingqi 2 ;

作者机构： 1.Xinjiang Agr Univ, Sch Mech & Elect Engn, Urumqi 830052, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

关键词： tea canopy segmentation; Deeplabv3+; visual navigation; navigation paths

期刊名称：ELECTRONICS （ 影响因子：2.6； 五年影响因子：2.6 ）

ISSN： 2079-9292

年卷期： 2025 年 14 卷 5 期

页码：

收录情况： SCI

摘要： During the process of autonomous tea harvesting, it is essential for the tea-harvesting robots to navigate along the tea canopy while obtaining real-time and precise information about these tea canopies. Considering that most tea gardens are located in hilly and mountainous areas, GNSS signals often encounter disturbances, and laser sensors provide insufficient information, which fails to meet the navigation requirements of tea-harvesting robots. This study develops a vision-based semantic segmentation method for the identification of tea canopies and the generation of navigation paths. The proposed CDSC-Deeplabv3+ model integrates a Convnext backbone network with the DenseASP_SP module for feature fusion and a CFF module for enhanced semantic segmentation. The experimental results demonstrate that our proposed CDSC-Deeplabv3+ model achieves mAP, mIoU, F1-score, and FPS metrics of 96.99%, 94.71%, 98.66%, and 5.0, respectively; both the accuracy and speed performance indicators meet the practical requirements outlined in this study. Among the three compared methods for fitting the navigation central line, RANSAC shows superior performance, with minimum average angle deviations of 2.02 degrees, 0.36 degrees, and 0.46 degrees at camera tilt angles of 50 degrees, 45 degrees, and 40 degrees, respectively, validating the effectiveness of our approach in extracting stable tea canopy information and generating navigation paths.

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