This paper presents an autonomous robot capable of picking strawberries continuously in polytunnels. Robotic harvesting in cluttered and unstructured environment remains a challenge. A novel obstacle‐separation algorithm was proposed to enable the harvesting system to pick strawberries that are located in clusters. The algorithm uses the gripper to push aside surrounding leaves, strawberries, and other obstacles. We present the theoretical method to generate pushing paths based on the surrounding obstacles. In addition to manipulation, an improved vision system is more resilient to lighting variations, which was developed based on the modeling of color against light intensity. Further, a low‐cost dual‐arm system was developed with an optimized harvesting sequence that increases its efficiency and minimizes the risk of collision. Improvements were also made to the existing gripper to enable the robot to pick directly into a market punnet, thereby eliminating the need for repacking. During tests on a strawberry farm, the robots first‐attempt success rate for picking partially surrounded or isolated strawberries ranged from 50% to 97.1%, depending on the growth situations. Upon an additional attempt, the pick success rate increased to a range of 75–100%. In the field tests, the system was not able to pick a target that was entirely surrounded by obstacles. This failure was attributed to limitations in the vision system as well as insufficient dexterity in the grippers. However, the picking speed improved upon previous systems, taking just 6.1 s for manipulation operation in the one‐arm mode and 4.6 s in the two‐arm mode.

中文翻译：

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自主的草莓采摘机器人：设计，开发，集成和现场评估

本文提出了一种自动机器人，该机器人能够连续地在多漏斗中采摘草莓。在混乱和非结构化的环境中进行机器人收获仍然是一个挑战。提出了一种新颖的障碍物分离算法，使收割系统能够采摘成簇的草莓。该算法使用the纸牙将周围的树叶，草莓和其他障碍物推开。我们提出了基于周围障碍物产生推挤路径的理论方法。除了操纵之外，一种改进的视觉系统还具有针对照明变化的弹性，这是基于颜色对光强度的建模而开发的。此外，开发了一种低成本的双臂系统，该系统具有优化的收割顺序，从而提高了收割效率，并最大程度地降低了发生碰撞的风险。还对现有的抓取器进行了改进，以使机器人可以直接将其插入市场的检举器，从而消除了重新包装的需要。在草莓农场的测试过程中，机器人会自动尝试采摘部分包围或隔离的草莓，其成功率在50％到97.1％之间，具体取决于生长情况。再次尝试后，选择成功率提高到75–100％。在现场测试中，系统无法选择完全被障碍物包围的目标。该失败归因于视觉系统的局限性以及抓爪的灵活性不足。但是，与以前的系统相比，拾取速度有所提高，单臂模式下的操纵操作仅需6.1 s，而两臂模式下的操纵仅需4.6 s。