番茄串收机械臂运动规划方法与试验  

Motion planning method and experiments of tomato bunch harvesting manipulator

作　　者：张勤[1] 刘丰溥 蒋先平 熊征 徐灿 Zhang Qin;Liu Fengpu;Jiang Xianping;Xiong Zheng;Xu Can(School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510641,China;Guangdong Institute of Modern Agriculture Equipment,Guangzhou 510630,China)

机构地区：[1]华南理工大学机械与汽车工程学院,广州510641 [2]广东省现代农业装备研究所,广州510630

出　　处：《农业工程学报》2021年第9期149-156,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：广东省重点领域研发计划资助(2019B020222002);2019年广东省乡村振兴战略专项(粤财农[2019]73号)。

摘　　要：番茄串的采收环境复杂,果实体积相对较大,机械臂采收运动路径规划不仅要考虑如何采摘,还需要考虑采摘后如何避开障碍,并从复杂环境中提取出番茄串。为此,该研究以温室栽培的番茄串采摘为对象,提出了基于空间分割的实时运动路径规划算法。首先通过聚类拟合环境中的枝条,简化空间障碍物;然后分割采摘空间,筛选可行采摘空间,并引入评价函数选取最优采摘空间,指导机械臂以合理有效的姿态完成采摘;最后在采摘任务的基础上加入实时避障子任务,引导机械臂躲避障碍完成任务,保证采摘番茄串任务安全无损。在以上研究的基础上,通过大量采收试验验证算法的有效性。试验结果表明:通过基于空间分割的实时运动路径规划算法,采收机器人的单串番茄采摘时间为12.51 s,且采摘成功率接近100%。与目前主流的采样算法RRT*-connect相比,单串番茄的采摘时间降低了31.23%,大幅提高了采摘效率。Tomato picking environment is characterized by an unstructured space covering most obstacles,such as branches and vines.It is difficult to accurately express in a regular way,particularly for the relatively large volume of tomato bunches.Therefore,the motion planning of the robotic arm in a harvesting manipulator needs to consider how to pick tomato bunches,while avoiding obstacles after cutting the tomato bunches,and finally extracting them from a complex actual environment.Most previous motion planning of tomato picking focused mainly on the obstacle-free moving to the position of picking stalk.But only a few studies reported the specific fruit extraction with an increase in volume after the end-effector of robotic arm gripping the tomato bunch.Taking the tomato bunch picking cultivated in the greenhouse as the research object,real-time motion planning with collision-free Optimal Picking Space(OPS)was proposed here using space segmentation.A reasonable and effective space was also selected for the robotic arm to implement the picking task,in advance to avoid the failure caused by fruit collision or beyond the working range of the manipulator.The specific procedure was as follows.1)Thousands of color pictures with tomato bunches were first collected.The YOLO-V3 model was used for training to obtain a better recognition network.An RGB-D camera was then used to capture the color and depth information of the environment.The trained YOLO-V3 model was to identify and locate the pixel position of the picking point for the tomato strings in the color map.Next,the internal and external parameters of the camera were cooperated to determine the three-dimensional position of the picking point for a tomato string.An improved density clustering was utilized to focus on the picking area near the picking point of the tomato bunch,while separate the multiple obstacles in the environment.A polynomial function was selected to fit the space curve of branch obstacles falling from top to bottom during the tomato cluster picking in a

关 键 词：机器人 收获 机械臂 路径规划 空间分割 最优采摘空间 串番茄 

分 类 号：TP241[自动化与计算机技术—检测技术与自动化装置]