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  • Vision-based self-adaptive gripping in a trimodal robotic sorting end-effector

Sadeghian, Rasoul, Shahin, Shahrooz and Sareh, Sina ORCID: https://orcid.org/0000-0002-9787-1798, 2022, Journal Article, Vision-based self-adaptive gripping in a trimodal robotic sorting end-effector IEEE Robotics and Automation Letters, 7 (2). pp. 2124-2131. ISSN 2377-3766

Abstract or Description:

Recyclable waste management, which includes sorting as a key process, is a crucial component of maintaining a sustainable ecosystem. The use of robots in sorting could significantly facilitate the production of secondary raw materials from waste in the sense of a recycling economy. However, due to the complex and heterogeneous types of the recyclable items, the conventional robotic gripping end-effectors, which typically come with a fixed structure, are unlikely to hold onto the full range of items to enable separation and recycling. To this end, a trimodal adaptive end-effector is proposed that can be integrated with robotic manipulators to improve their gripping versatility. The end-effector can deploy effective modes of gripping to different objects in response to their size and porosity via gripping mechanisms based on Nano Polyurethane (PU) adhesive gels, pumpless vacuum suction, and radially deployable claws. While the end-effector’s mechanical design allows the three gripping modes to be deployed independently or in conjunction with one another, this work aims at deploying modes that are effective for gripping onto the recyclable item. In order to decide on the suitable modes of gripping, a real-time vision system is designed to measure the size and porosity of the recyclable items and advise on a suitable combination of gripping modes to be deployed. Integrated current sensors provide an indication of successful gripping and releasing of the recyclable items. The results of the experiments confirmed the ability of our vision-based approach in identifying suitable gripping modes in real-time, the deployment of the relevant mechanisms and successful gripping onto a maximum of 84.8% (single-mode), 90.9% (dual-mode) and 96.9% (triple-mode) of a specified set of recyclable items.

Official URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&ar...
Subjects: Other > Engineering > H600 Electronic and Electrical Engineering > H670 Robotics and Cybernetics > H671 Robotics
School or Centre: Other
Research & Innovation
School of Design
Funders: EPSRC [EP/S001840/1]
Identification Number or DOI: https://doi.org/10.1109/LRA.2022.3140793
Additional Information:

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Uncontrolled Keywords: Robots; End effectors; Sorting; Grippers; Feature extraction; Deep learning; Robot sensing systems; Adaptive autonomous systems; grasping; machine vision; end effectors
Date Deposited: 01 Feb 2022 12:11
Last Modified: 01 Feb 2022 12:11
URI: https://researchonline.rca.ac.uk/id/eprint/4974
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