This work presents a machine vision system for the localization of strawberries and environment perception in a strawberry-harvesting robot for use in table-top strawberry production. A deep convolutionalneuralnetworkforsegmentationisutilizedtodetectthestrawberries.Segmentedstrawberries are localized through coordinate transformation, density base point clustering and the proposed location approximation method. To avoid collisions between the gripper and fixed obstacles, the safe manipulation region is limited to the space in front of the table and underneath the strap. Therefore, a safe region classification algorithm, based on Hough Transform algorithm, is proposed to segment the strap masks into a belt region in order to identify the pickable strawberries located underneath the strap. Similarly, a safe region classification algorithm is proposed for the table, to calculate its points in 3D and fit the points onto a 3D plane based on the 3D point cloud, so that pickable strawberries in front of the table can be identified. Experimentaltestsshowedthatthealgorithmcouldaccuratelyclassifyripeandunripestrawberriesandcould identify whether the strawberries are within the safe region for harvesting. Furthermore, harvester robot’s optimized localization method could accurately locate the strawberry targets with a picking accuracy rate of 74.1% in modified situations. ; publishedVersion
Fruit localization and environment perception for strawberry harvesting robots
2019-10-07
cristin:1734374
147642-147652 ; 7 ; IEEE Access
Article (Journal)
Electronic Resource
English
DDC: | 629 |
Fruit Harvesting Robots in Japan
British Library Conference Proceedings | 1995
|Fruit Harvesting Robots in Japan
Online Contents | 1996
|Obstacle-Avoidance Algorithm for Fruit Harvesting Robots
British Library Conference Proceedings | 1995
|Towards Automated Strawberry Harvesting: Identifying the Picking Point
British Library Conference Proceedings | 2017
|