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

Abstract

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°, 0.36°, and 0.46° at camera tilt angles of 50°, 45°, and 40°, respectively, validating the effectiveness of our approach in extracting stable tea canopy information and generating navigation paths.