Solving peg-in-hole tasks by human demonstration and exception strategies

Fares Abu-Dakka, Bojan Nemec, Aljaz Kramberger, Anders Glent Buch, Norbert Krüger, Ales Ude

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract





Purpose

– The purpose of this paper is to propose a new algorithm based on programming by demonstration and exception strategies to solve assembly tasks such as peg-in-hole.




Design/methodology/approach

– Data describing the demonstrated tasks are obtained by kinesthetic guiding. The demonstrated trajectories are transferred to new robot workspaces using three-dimensional (3D) vision. Noise introduced by vision when transferring the task to a new configuration could cause the execution to fail, but such problems are resolved through exception strategies.




Findings

– This paper demonstrated that the proposed approach combined with exception strategies outperforms traditional approaches for robot-based assembly. Experimental evaluation was carried out on Cranfield Benchmark, which constitutes a standardized assembly task in robotics. This paper also performed statistical evaluation based on experiments carried out on two different robotic platforms.




Practical implications

– The developed framework can have an important impact for robot assembly processes, which are among the most important applications of industrial robots. Our future plans involve implementation of our framework in a commercially available robot controller.




Originality/value

– This paper proposes a new approach to the robot assembly based on the Learning by Demonstration (LbD) paradigm. The proposed framework enables to quickly program new assembly tasks without the need for detailed analysis of the geometric and dynamic characteristics of workpieces involved in the assembly task. The algorithm provides an effective disturbance rejection, improved stability and increased overall performance. The proposed exception strategies increase the success rate of the algorithm when the task is transferred to new areas of the workspace, where it is necessary to deal with vision noise and altered dynamic characteristics of the task.
Original languageEnglish
JournalIndustrial Robot: the international journal of robotics research and application
Volume41
Issue number6
Pages (from-to)575-584
ISSN0143-991X
DOIs
Publication statusPublished - 2014

Cite this

@article{6b830616a39543b7905bfe77d1e066cc,
title = "Solving peg-in-hole tasks by human demonstration and exception strategies",
abstract = "Purpose– The purpose of this paper is to propose a new algorithm based on programming by demonstration and exception strategies to solve assembly tasks such as peg-in-hole. Design/methodology/approach– Data describing the demonstrated tasks are obtained by kinesthetic guiding. The demonstrated trajectories are transferred to new robot workspaces using three-dimensional (3D) vision. Noise introduced by vision when transferring the task to a new configuration could cause the execution to fail, but such problems are resolved through exception strategies. Findings– This paper demonstrated that the proposed approach combined with exception strategies outperforms traditional approaches for robot-based assembly. Experimental evaluation was carried out on Cranfield Benchmark, which constitutes a standardized assembly task in robotics. This paper also performed statistical evaluation based on experiments carried out on two different robotic platforms. Practical implications– The developed framework can have an important impact for robot assembly processes, which are among the most important applications of industrial robots. Our future plans involve implementation of our framework in a commercially available robot controller. Originality/value– This paper proposes a new approach to the robot assembly based on the Learning by Demonstration (LbD) paradigm. The proposed framework enables to quickly program new assembly tasks without the need for detailed analysis of the geometric and dynamic characteristics of workpieces involved in the assembly task. The algorithm provides an effective disturbance rejection, improved stability and increased overall performance. The proposed exception strategies increase the success rate of the algorithm when the task is transferred to new areas of the workspace, where it is necessary to deal with vision noise and altered dynamic characteristics of the task.",
author = "Fares Abu-Dakka and Bojan Nemec and Aljaz Kramberger and Buch, {Anders Glent} and Norbert Kr{\"u}ger and Ales Ude",
year = "2014",
doi = "10.1108/IR-07-2014-0363",
language = "English",
volume = "41",
pages = "575--584",
journal = "Industrial Robot: the international journal of robotics research and application",
issn = "0143-991X",
publisher = "JAI Press",
number = "6",

}

Solving peg-in-hole tasks by human demonstration and exception strategies. / Abu-Dakka, Fares; Nemec, Bojan; Kramberger, Aljaz; Buch, Anders Glent; Krüger, Norbert; Ude, Ales.

In: Industrial Robot: the international journal of robotics research and application, Vol. 41, No. 6, 2014, p. 575-584.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Solving peg-in-hole tasks by human demonstration and exception strategies

AU - Abu-Dakka, Fares

AU - Nemec, Bojan

AU - Kramberger, Aljaz

AU - Buch, Anders Glent

AU - Krüger, Norbert

AU - Ude, Ales

PY - 2014

Y1 - 2014

N2 - Purpose– The purpose of this paper is to propose a new algorithm based on programming by demonstration and exception strategies to solve assembly tasks such as peg-in-hole. Design/methodology/approach– Data describing the demonstrated tasks are obtained by kinesthetic guiding. The demonstrated trajectories are transferred to new robot workspaces using three-dimensional (3D) vision. Noise introduced by vision when transferring the task to a new configuration could cause the execution to fail, but such problems are resolved through exception strategies. Findings– This paper demonstrated that the proposed approach combined with exception strategies outperforms traditional approaches for robot-based assembly. Experimental evaluation was carried out on Cranfield Benchmark, which constitutes a standardized assembly task in robotics. This paper also performed statistical evaluation based on experiments carried out on two different robotic platforms. Practical implications– The developed framework can have an important impact for robot assembly processes, which are among the most important applications of industrial robots. Our future plans involve implementation of our framework in a commercially available robot controller. Originality/value– This paper proposes a new approach to the robot assembly based on the Learning by Demonstration (LbD) paradigm. The proposed framework enables to quickly program new assembly tasks without the need for detailed analysis of the geometric and dynamic characteristics of workpieces involved in the assembly task. The algorithm provides an effective disturbance rejection, improved stability and increased overall performance. The proposed exception strategies increase the success rate of the algorithm when the task is transferred to new areas of the workspace, where it is necessary to deal with vision noise and altered dynamic characteristics of the task.

AB - Purpose– The purpose of this paper is to propose a new algorithm based on programming by demonstration and exception strategies to solve assembly tasks such as peg-in-hole. Design/methodology/approach– Data describing the demonstrated tasks are obtained by kinesthetic guiding. The demonstrated trajectories are transferred to new robot workspaces using three-dimensional (3D) vision. Noise introduced by vision when transferring the task to a new configuration could cause the execution to fail, but such problems are resolved through exception strategies. Findings– This paper demonstrated that the proposed approach combined with exception strategies outperforms traditional approaches for robot-based assembly. Experimental evaluation was carried out on Cranfield Benchmark, which constitutes a standardized assembly task in robotics. This paper also performed statistical evaluation based on experiments carried out on two different robotic platforms. Practical implications– The developed framework can have an important impact for robot assembly processes, which are among the most important applications of industrial robots. Our future plans involve implementation of our framework in a commercially available robot controller. Originality/value– This paper proposes a new approach to the robot assembly based on the Learning by Demonstration (LbD) paradigm. The proposed framework enables to quickly program new assembly tasks without the need for detailed analysis of the geometric and dynamic characteristics of workpieces involved in the assembly task. The algorithm provides an effective disturbance rejection, improved stability and increased overall performance. The proposed exception strategies increase the success rate of the algorithm when the task is transferred to new areas of the workspace, where it is necessary to deal with vision noise and altered dynamic characteristics of the task.

U2 - 10.1108/IR-07-2014-0363

DO - 10.1108/IR-07-2014-0363

M3 - Journal article

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EP - 584

JO - Industrial Robot: the international journal of robotics research and application

JF - Industrial Robot: the international journal of robotics research and application

SN - 0143-991X

IS - 6

ER -