Estimation of grasp type and determining grasping points for Adaptive Grasping Device

The article describes the grasping point algorithm (GPA), aimed to determine points for picking up objects by a manipulator equipped with a proprietary gripper enabling three picking modes (suction cup, two fingers, three fingers). The paper outlines the topic of unknown object picking using various types of grippers, both from a stack of objects and stand-alone items. It describes details of four methods available in the literature. Presented GPA algorithm consists of four separate functions dedicated to appropriate grasping methods chosen based on a decisionmaking process. The dedicated functions has been described in detail and tested. Used methods have been tested manually on 15 different objects. GPA algorithm has been evaluated in realworld tests as well. The presented tests showed efficiency of approximately 92% in the case of moving previously unknown objects from the stack to the target cuvette.

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Publication Details

Journal
Archives of Control Sciences
Published
2025-03-14
DOI
https://doi.org/10.24425/acs.2025.153959
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

Estimation of grasp type and determining grasping points for Adaptive Grasping Device

Michał Czubenko, Rafał Okuński, Piotr Łyczko
Archives of Control Sciences
Robot Manipulation and Learning
article

Estimation of grasp type and determining grasping points for Adaptive Grasping Device

Michał Czubenko, Rafał Okuński, Piotr Łyczko
article en

Abstract

The article describes the grasping point algorithm (GPA), aimed to determine points for picking up objects by a manipulator equipped with a proprietary gripper enabling three picking modes (suction cup, two fingers, three fingers). The paper outlines the topic of unknown object picking using various types of grippers, both from a stack of objects and stand-alone items. It describes details of four methods available in the literature. Presented GPA algorithm consists of four separate functions dedicated to appropriate grasping methods chosen based on a decisionmaking process. The dedicated functions has been described in detail and tested. Used methods have been tested manually on 15 different objects. GPA algorithm has been evaluated in realworld tests as well. The presented tests showed efficiency of approximately 92% in the case of moving previously unknown objects from the stack to the target cuvette.

Archives of Control Sciences
Gdańsk University of Technology (PL)
Openalex Percentile: Top 95%
Robot Manipulation and Learning
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