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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

<a href="/cs/project/EF17_049%2F0008394" target="_blank" >EF17_049/0008394: Spolupráce Univerzity Pardubice a aplikační sféry v aplikačně orientovaném výzkumu lokačních, detekčních a simulačních systémů pro dopravní a přepravní procesy (PosiTrans)</a><br>

Návaznosti

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

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Journal of Intelligent and Robotic Systems: Theory and Applications

ISSN

0921-0296

e-ISSN

1573-0409

Svazek periodika

110

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

23

Strana od-do

—

Kód UT WoS článku

001318676700001

EID výsledku v databázi Scopus

2-s2.0-85199867198