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Design and Kinematic Modeling of Grape Picking Robot Arm

Published: 21 December 2023 Publication History

Abstract

Aiming at the application of robots in intelligent agriculture, this paper proposes a method of picking grapes by robots based on deep learning, uses depth camera for visual positioning of grapes, and designs a picking end-effector for picking tests. Picking experiments show that the method proposed in this paper has high detection accuracy, accurate grape rod positioning, high robot picking accuracy and good availability.

References

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Jizhan L, Jin L, Shengyi Z, (2023). Design of a Virtual Multi-Interaction Operation System for Hand–Eye Coordination of Grape Harvesting Robots[J]. Agronomy, 13(3).
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Luo L, Tang Y, Lu Q, (2018). A vision methodology for harvesting robot to detect cutting points on peduncles of double overlapping grape clusters in a vineyard[J]. Computers in Industry, 99: 130-139.
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Zhang T, Huang Z, You W, (2019). An Autonomous Fruit and Vegetable Harvester with a Low-Cost Gripper Using a 3D Sensor[J]. Sensors, 20(1).
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Abbas M, Narayan J, Banerjee S, (2020). AlexNet based Real-Time Detection and Segregation of Household Objects using Scorbot[C]. 4th IEEE International Conference on Computational Intelligence and Networks (CINE 2020).
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Kebria P M, Al-Wais S, Abdi H, (2017). Kinematic and dynamic modelling of UR5 manipulator[C]. 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).
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Meng C, Feng C, Wen Z, (2022). Design of Flexible Spherical Fruit and Vegetable Picking End-effector Based on Vision Recognition[J]. Journal of Physics: Conference Series, 2246(1).
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Badeka E, Kalampokas T, Vrochidou E, (2021). Real-time vineyard trunk detection for a grape harvesting robot via deep learning[C]. Thirteenth International Conference on Machine Vision.
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Wei L, Tao Z, Yun M, (2023). 3D Street Object Detection from Monocular Images Using Deep Learning and Depth Information[J]. jaciii, 27(2).
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Teke B (2018). Real-time Target Tracking and Following with UR5 Collaborative Robot Arm[J].

Cited By

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  • (2024)Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk ManagementApplied Sciences10.3390/app14221027714:22(10277)Online publication date: 8-Nov-2024

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Published In

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CSAE '23: Proceedings of the 7th International Conference on Computer Science and Application Engineering
October 2023
358 pages
ISBN:9798400700590
DOI:10.1145/3627915
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 December 2023

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Author Tags

  1. End-effector
  2. Kinematic Modelling
  3. Picking Robot
  4. Visual Localization

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  • Research-article
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CSAE 2023

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Overall Acceptance Rate 368 of 770 submissions, 48%

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Cited By

View all
  • (2024)Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk ManagementApplied Sciences10.3390/app14221027714:22(10277)Online publication date: 8-Nov-2024

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