![]() ![]() The indoor accuracy experiment shows that when the degree of obscuration of grape clusters by leaves increases, the vision algorithm based on the geometric contours of grape clusters can still meet the demands of harvesting tasks. To improve the harvesting efficiency, some key high-speed harvesting technologies were adopted, such as the harvesting sequence decision based on the “sequential mirroring method” of grape cluster depth information, “one-eye and dual-arm” high-speed visual servo, dual arm action sequence decision, and optimization of the “visual end effector” large tolerance combination in a natural environment. Robotic arm and camera view analysis of the workspace harvesting process was performed using MATLAB, and it can be concluded that the structural design of this robot meets the grape harvesting requirements with a standard trellis. ![]() Based on the characteristics of the harvesting environment, such as the small gap between grape clusters, standard trellis, and vertical suspension of clusters, the configuration of the dual-arm harvesting robot is reasonably designed and analyzed, and the overall configuration of the machine and the installation position of key components are derived. The design and experimental analysis of a dual-arm high-speed grape-harvesting robot were carried out to address the limitations of low picking efficiency and high grape breakage rate of multijoint robotic arms. It is extremely necessary to achieve the rapid harvesting of table grapes planted with a standard trellis in the grape industry. Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Zhenjiang, China.Yingxing Jiang Jizhan Liu * Jie Wang Wuhao Li Yun Peng Haiyong Shan
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