Abstract Selectively picking a target fruit surrounded by obstacles is one of the major challenges for fruit harvesting robots. Different from traditional obstacle avoidance methods, this paper presents an active obstacle separation strategy that combines push and drag motions based on 3D visual perception to separate obstacles from the target. We define a region of interest 3D point cloud with a number of sub-blocks around the target to determine the presence or absence of obstacles and generate the separation paths accordingly. A linear push is used to clear the obstacles from the area below the target, while a zig-zag push that contains several linear motions is proposed to push aside more dense obstacles. The zig-zag push can generate multi-directional pushes and the side-to-side motion can break the static contact force between the target and obstacles, thus helping the gripper to receive a target in more complex situations. Moreover, we propose a novel drag operation to address the issue of mis-capturing obstacles located above the target, in which the gripper drags the target to a place with fewer obstacles and then pushes back to move the obstacles aside for further detachment. Furthermore, an image processing pipeline consisting of color thresholding, object detection using deep learning and point cloud operation, is developed to implement the proposed method on a newly developed harvesting robot. Field tests show that the proposed method can improve the picking performance substantially. This method helps to enable complex clusters of fruits to be harvested with a higher success rate than conventional methods.

中文翻译：

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一种机器人聚类水果采摘障碍物分离方法

摘要 选择性采摘被障碍物包围的目标水果是水果收获机器人面临的主要挑战之一。与传统的避障方法不同，本文提出了一种基于3D视觉感知的推、拖相结合的主动障碍分离策略，将障碍物与目标分离。我们用目标周围的多个子块定义一个感兴趣的 3D 点云区域，以确定是否存在障碍物并相应地生成分离路径。线性推用于清除目标下方区域的障碍物，而包含多个线性运动的锯齿形推用于推开更密集的障碍物。之字形推动可以产生多向推动，左右运动可以打破目标与障碍物之间的静态接触力，从而帮助抓手在更复杂的情况下接收目标。此外，我们提出了一种新颖的拖动操作来解决错误捕获位于目标上方的障碍物的问题，其中夹具将目标拖动到障碍物较少的地方，然后向后推以将障碍物移到一边以进一步脱离。此外，开发了由颜色阈值、使用深度学习和点云操作的对象检测组成的图像处理管道，以在新开发的收割机器人上实施所提出的方法。现场测试表明，所提出的方法可以显着提高拣货性能。