Jiang Yingxing, Liu Jizhan, Wang Jie, Li Wuhao, Peng Yun, Shan Haiyong
Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Zhenjiang, China.
Front Plant Sci. 2022 Sep 20;13:881904. doi: 10.3389/fpls.2022.881904. eCollection 2022.
It is extremely necessary to achieve the rapid harvesting of table grapes planted with a standard trellis in the grape industry. 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. 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. 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. 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. 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. The motion positioning average errors of the left and right robotic arms were (: 2.885 mm, : 3.972 mm, : 2.715 mm) and (: 2.471 mm, : 3.289 mm, : 3.775 mm), respectively, and the average dual-arm harvesting time in one grape cluster was 8.45 s. The field performance test verifies that the average harvesting cycle of the robot with both arms reached 9 s/bunch, and the success rate of bunch identification and harvesting success rate reached 88 and 83%, respectively, which were significantly better than those of existing harvesting robots worldwide.
在葡萄产业中，实现标准棚架栽培的鲜食葡萄快速采收极为必要。为解决多关节机器人手臂采摘效率低、葡萄破损率高的问题，开展了双臂高速葡萄采摘机器人的设计与实验分析。基于葡萄采收环境特点，如葡萄串间距小、标准棚架以及果串垂直悬挂等，对双臂采摘机器人的结构进行了合理设计与分析，得出了整机总体结构及关键部件的安装位置。利用MATLAB对工作空间采收过程进行了机器人手臂和摄像机视野分析，结果表明该机器人的结构设计满足标准棚架葡萄采收要求。为提高采收效率，采用了一些关键的高速采收技术，如基于葡萄串深度信息“顺序镜像法”的采收顺序决策、“单目双臂”高速视觉伺服、双臂动作顺序决策以及自然环境下“视觉末端执行器”大公差组合优化等。室内精度实验表明，当葡萄串被叶片遮挡程度增加时，基于葡萄串几何轮廓的视觉算法仍能满足采收任务需求。左右机器人手臂的运动定位平均误差分别为（: 2.885毫米，: 3.972毫米，: 2.715毫米）和（: 2.471毫米，: 3.289毫米，: 3.775毫米），采摘一串葡萄的双臂平均采收时间为8.45秒。田间性能测试验证了该双臂机器人的平均采收周期达到9秒/串，果串识别成功率和采收成功率分别达到88%和83%，显著优于全球现有的采摘机器人。
