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ShakingBot: dynamic manipulation for bagging

Published online by Cambridge University Press:  04 January 2024

Ningquan Gu
Affiliation:
School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, China
Zhizhong Zhang
Affiliation:
School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, China
Ruhan He*
Affiliation:
School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, China
Lianqing Yu
Affiliation:
School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan, China
*
Corresponding author: Ruhan He; Email: heruhan@wtu.edu.cn

Abstract

Bag manipulation through robots is complex and challenging due to the deformability of the bag. Based on the dynamic manipulation strategy, we propose a new framework, ShakingBot, for the bagging tasks. ShakingBot utilizes a perception module to identify the key region of the plastic bag from arbitrary initial configurations. According to the segmentation, ShakingBot iteratively executes a novel set of actions, including Bag Adjustment, Dual-arm Shaking, and One-arm Holding, to open the bag. The dynamic action, Dual-arm Shaking, can effectively open the bag without the need to take into account the crumpled configuration. Then, the robot inserts the items and lifts the bag for transport. We perform our method on a dual-arm robot and achieve a success rate of 21/33 for inserting at least one item across various initial bag configurations. In this work, we demonstrate the performance of dynamic shaking action compared to the quasi-static manipulation in the bagging task. We also show that our method generalizes to variations despite the bag’s size, pattern, and color. Supplementary material is available at https://github.com/zhangxiaozhier/ShakingBot.

Type
Research Article
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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