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This paper presents the development and evaluation of a robot for harvesting strawberries (Fragaria × ananassa) grown on table-tops in polytunnels. The robot is comprised of a newly-designed gripper mounted on an industrial arm which in turn is mounted on a mobile base along with an RGB-D camera. The novel cable-driven gripper can open fingers to “swallow” a target. Since it is designed to target the fruit not the stem, it just requires the fruit location for picking. Moreover, equipped with internal sensors, the gripper can sense and correct for positional errors, and is robust to the localisation errors introduced by the vision module. Another important feature of the gripper is the internal container that is used to collect berries during picking. Since the manipulator does not need to go back and forth between each berry and a separate punnet, picking time is reduced significantly. The vision system uses colour thresholding combined with screening of the object area and the depth range to select ripe and reachable strawberries, which is fast for processing. These components are integrated into a complete system whose performance is analysed starting with the four main failure cases of the vision system: undetected, duplicate detections, inaccurate localisation and segmentation failure. The integration enables the robot to harvest continuously by moving the platform with a joystick. Field experiments show the average cycle time of continuous single strawberry picking is 7.5 s and 10.6 s when including all procedures. Furthermore, the robot is able to pick isolated strawberries with a close-to-perfect success rate (96.8%). However, in farm settings, the average picking success rate is 53.6%, and 59.0% when including “success with damage”, testing on the strawberry cultivar of “FAVORI”. The failure cases are analysed and most failures are found when picking strawberries in clusters, in which both the detection algorithm and the gripper struggles to separate the berries.
Xiong et al. (Fri,) studied this question.