Towards an Efficient Tomato Harvesting Robot: 3D Perception, Manipulation, and End-Effector

Fruit and vegetable harvesting robots have been widely studied and developed in recent years. However, despite extensive research commercial tomato harvesting robots still remain a challenge. In this paper, we propose an efficient tomato harvesting robot that combines the principle of 3D perception, Manipulation, and an End-effector. For this robot, tomatoes are detected based on deep learning, after which 3D coordinates of the target crop are extracted and motion control of the manipulator based on 3D coordination. In addition, a suction pad featuring the kirigami pattern, which is a part of the suction gripper, was developed to grip individual tomatoes in clusters. A scissor-shaped cutting module with an assist unit, which is used to overcome structural limitations and implement effective cutting, was also desinged and tested. The proposed tomato harvesting robot was validated and evaluated on a laboratory testbed basd on the performance of each component. Therefore, in this study, we propose and verify a new robot design for the effective harvesting of tomatoes.

Automated summary

This study proposes an efficient tomato harvesting robot design that combines the principles of 3D perception, manipulation, and an end-effector. The robot detects tomatoes based on deep learning and controls the manipulator's motion based on 3D coordination. Additionally, a suction pad with a kirigami pattern and a scissor-shaped cutting module were developed for gripping and cutting tomatoes effectively.