  Currently, truss tomato weighing and packaging require significant manual
work. The main obstacle to automation lies in the difficulty of developing a
reliable robotic grasping system for already harvested trusses. We propose a
method to grasp trusses that are stacked in a crate with considerable clutter,
which is how they are commonly stored and transported after harvest. The method
consists of a deep learning-based vision system to first identify the
individual trusses in the crate and then determine a suitable grasping location
on the stem. To this end, we have introduced a grasp pose ranking algorithm
with online learning capabilities. After selecting the most promising grasp
pose, the robot executes a pinch grasp without needing touch sensors or
geometric models. Lab experiments with a robotic manipulator equipped with an
eye-in-hand RGB-D camera showed a 100% clearance rate when tasked to pick all
trusses from a pile. 93% of the trusses were successfully grasped on the first
try, while the remaining 7% required more attempts.

Robotics;Artificial Intelligence;Computer Vision and Pattern Recognition