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

Publikation: Bidrag til bog/antologi/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

Resumé

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.
OriginalsprogEngelsk
TitelIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Antal sider8
Publikationsdato2019
StatusUdgivet - 2019

Fingeraftryk

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

Citer dette

@inproceedings{5c23da5ebf6044fcbc482a9453a94626,
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)",

}

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.

Publikation: Bidrag til bog/antologi/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

TY - GEN

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

AU - Hagelskjær, Frederik

AU - Kramberger, Aljaz

AU - Wolniakowski, Adam

AU - Savarimuthu, Thiusius Rajeeth

AU - Krüger, Norbert

PY - 2019

Y1 - 2019

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

KW - simulation

KW - optimization

M3 - Article in proceedings

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

ER -