Fruit pose recognition and directional orderly grasping strategies for tomato harvesting robots

Abstract

The prospect of robotic harvesting in greenhouse tomato production has received much attention in the tomato industry in recent years, but research into the replacement of manual harvesting with this technology is still inadequate. There is still a large gap between robotic harvesting accuracy and harvesting efficiency compared to manual harvesting. The aim of this study is to improve the success rate and efficiency of robotic harvesting by accurately identifying the position of the tomato fruit and estimating the grasping pose. Based on a specially designed adsorption and clamping integrated manipulator, we develop the optimal sorting algorithm and fruit nearest neighbour positioning algorithm and design directional grasping and sequential picking control strategies that can reduce the impact of dense fruit on the accurate grasping of the manipulator. The results evaluated in the industrial greenhouse show that the YOLOv5m-based fruit bunch and fruit recognition accuracies are 90.2 % and 97.3 %, respectively. Based on the optimized picking strategy, the impact of collision on the manipulator's grasp was substantially reduced, and the fruit harvesting success rate increased to 72.1 %, while the average harvesting time reached 14.6 s for a single fruit. The research suggests that with moderate improvements in harvesting technology, the use of robotic systems in the tomato industry will gradually become a reality.