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

Abstract

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.