Memory Efficient Deep Learning-Based Grasping Point Detection of Nontrivial Objects for Robotic Bin Picking

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F24%3A39922178" target="_blank" >RIV/00216275:25530/24:39922178 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s10846-024-02153-9" target="_blank" >https://link.springer.com/article/10.1007/s10846-024-02153-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10846-024-02153-9" target="_blank" >10.1007/s10846-024-02153-9</a>

Result language

angličtina

Original language name

Memory Efficient Deep Learning-Based Grasping Point Detection of Nontrivial Objects for Robotic Bin Picking

Original language description

Picking up non-trivial objects from a bin with a robotic arm is a common task of modern industrial processes. Here, an efficient data-driven method of grasping point detection, based on an attention squeeze parallel U-shaped neural network (ASP U-Net) for the bin picking task, is proposed. The method directly provides all necessary information about the feasible grasping points of objects, which are randomly or regularly arranged in a bin with side walls. Moreover, the method is able to evaluate and select the optimal grasping point among the feasible ones for two types of end effectors, i.e., a vacuum cup and a parallel gripper. The key element of the utilized ASP U-Net neural network is the transformation of a single RGB-Depth image of the bin containing nontrivial objects into a schematic grey-scale frame, where the positions and poses of the grasping points are coded into gradient geometric shapes. The experiments carried out in this study include a comprehensive set of scenes with randomly scattered, ordered, and semi-ordered objects arranged in impeccable or deformed bins. The results indicate outstanding accuracy with more than acceptable computational requirements. Additionally, the scaling possibilities of the method can offer extremely lightweight implementations, applicable, for example, to battery-powered edge-computing devices with low RAM capacity.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20204 - Robotics and automatic control

Project

<a href="/en/project/EF17_049%2F0008394" target="_blank" >EF17_049/0008394: Cooperation in Applied Research between the University of Pardubice and companies, in the Field of Positioning, Detection and Simulation Technology for Transport Systems (PosiTrans)</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Intelligent and Robotic Systems: Theory and Applications

ISSN

0921-0296

e-ISSN

1573-0409

Volume of the periodical

110

Issue of the periodical within the volume

3

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

23

Pages from-to

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UT code for WoS article

001318676700001

EID of the result in the Scopus database

2-s2.0-85199867198