Combined Optimization of Gripper Finger Design and Pose Estimation Processes for Advanced Industrial Assembly

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Vision systems are often used jointly with robotic manipulators to perform automated tasks in industrial applications. Still, the correct set up of such workcells is difficult and requires significant resources. One of the main challenges, when implementing such systems in industrial use cases, is the pose uncertainties presented by the vision system which have to be handled by grasping. In this paper, we present a framework for the design and analysis of optimal gripper finger designs and vision parameters. The proposed framework consists of two parallel methods which rely on vision and grasping simulation to provide an initial estimation of the uncertainty compensation capabilities of the designs. In case the compensation is not feasible with the initial design, an optimization process is introduced, to select the optimal pose estimation parameters and finger designs for the presented task.
The proposed framework was evaluated in dynamic simulation and implemented in a real industrial use case.
Original languageEnglish
Title of host publicationIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Number of pages8
Publication date2019
Publication statusPublished - 2019
EventIEEE/RSJ International Conference on Intelligent Robots and Systems - Macau, Macao
Duration: 4. Nov 20198. Nov 2019

Conference

ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems
CountryMacao
CityMacau
Period04/11/201908/11/2019

Fingerprint

Grippers
Parameter estimation
Industrial applications
Manipulators
Robotics
Computer simulation
Compensation and Redress
Uncertainty

Keywords

  • Gripper design
  • pose estimation
  • simulation
  • optimization

Cite this

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title = "Combined Optimization of Gripper Finger Design and Pose Estimation Processes for Advanced Industrial Assembly",
abstract = "Vision systems are often used jointly with robotic manipulators to perform automated tasks in industrial applications. Still, the correct set up of such workcells is difficult and requires significant resources. One of the main challenges, when implementing such systems in industrial use cases, is the pose uncertainties presented by the vision system which have to be handled by grasping. In this paper, we present a framework for the design and analysis of optimal gripper finger designs and vision parameters. The proposed framework consists of two parallel methods which rely on vision and grasping simulation to provide an initial estimation of the uncertainty compensation capabilities of the designs. In case the compensation is not feasible with the initial design, an optimization process is introduced, to select the optimal pose estimation parameters and finger designs for the presented task.The proposed framework was evaluated in dynamic simulation and implemented in a real industrial use case.",
keywords = "Gripper design, pose estimation, simulation, optimization",
author = "Frederik Hagelskj{\ae}r and Aljaz Kramberger and Adam Wolniakowski and Savarimuthu, {Thiusius Rajeeth} and Norbert Kr{\"u}ger",
year = "2019",
language = "English",
booktitle = "IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)",

}

Hagelskjær, F, Kramberger, A, Wolniakowski, A, Savarimuthu, TR & Krüger, N 2019, Combined Optimization of Gripper Finger Design and Pose Estimation Processes for Advanced Industrial Assembly. in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE/RSJ International Conference on Intelligent Robots and Systems, Macau, Macao, 04/11/2019.

Combined Optimization of Gripper Finger Design and Pose Estimation Processes for Advanced Industrial Assembly. / Hagelskjær, Frederik; Kramberger, Aljaz; Wolniakowski, Adam; Savarimuthu, Thiusius Rajeeth; Krüger, Norbert.

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2019.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

TY - GEN

T1 - Combined Optimization of Gripper Finger Design and Pose Estimation Processes for Advanced Industrial Assembly

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AU - Kramberger, Aljaz

AU - Wolniakowski, Adam

AU - Savarimuthu, Thiusius Rajeeth

AU - Krüger, Norbert

PY - 2019

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N2 - Vision systems are often used jointly with robotic manipulators to perform automated tasks in industrial applications. Still, the correct set up of such workcells is difficult and requires significant resources. One of the main challenges, when implementing such systems in industrial use cases, is the pose uncertainties presented by the vision system which have to be handled by grasping. In this paper, we present a framework for the design and analysis of optimal gripper finger designs and vision parameters. The proposed framework consists of two parallel methods which rely on vision and grasping simulation to provide an initial estimation of the uncertainty compensation capabilities of the designs. In case the compensation is not feasible with the initial design, an optimization process is introduced, to select the optimal pose estimation parameters and finger designs for the presented task.The proposed framework was evaluated in dynamic simulation and implemented in a real industrial use case.

AB - Vision systems are often used jointly with robotic manipulators to perform automated tasks in industrial applications. Still, the correct set up of such workcells is difficult and requires significant resources. One of the main challenges, when implementing such systems in industrial use cases, is the pose uncertainties presented by the vision system which have to be handled by grasping. In this paper, we present a framework for the design and analysis of optimal gripper finger designs and vision parameters. The proposed framework consists of two parallel methods which rely on vision and grasping simulation to provide an initial estimation of the uncertainty compensation capabilities of the designs. In case the compensation is not feasible with the initial design, an optimization process is introduced, to select the optimal pose estimation parameters and finger designs for the presented task.The proposed framework was evaluated in dynamic simulation and implemented in a real industrial use case.

KW - Gripper design

KW - pose estimation

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M3 - Article in proceedings

BT - IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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