A Vision-Guided Robotic System for Grasping Harvested Tomato Trusses in
  Cluttered Environments
Luuk van den Bent Tomás Coleman Robert Babuska
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.