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Motion Generators Combined with Behavior Trees: A Novel Approach to Skill Modelling

Rovida, Francesco ; Wuthier, David LU ; Grossmann, Bjarne ; Fumagalli, Matteo and Krüger, Volker LU (2018) p.5964-5971
Abstract
Task level programming based on skills has often been proposed as a mean to decrease programming complexity of industrial robots. Several models are based on encapsulating complex motions into self-contained primitive blocks. A semantic skill is then defined as a deterministic sequence of these primitives. A major limitation is that existing frameworks do not support the coordination of concurrent motion primitives with possible interference. This decreases their reusability and scalability in unstructured environments where a dynamic and reactive adaptation of motions is often required. This paper presents a novel framework that generates adaptive behaviors by modeling skills as concurrent motion primitives activated dynamically when... (More)
Task level programming based on skills has often been proposed as a mean to decrease programming complexity of industrial robots. Several models are based on encapsulating complex motions into self-contained primitive blocks. A semantic skill is then defined as a deterministic sequence of these primitives. A major limitation is that existing frameworks do not support the coordination of concurrent motion primitives with possible interference. This decreases their reusability and scalability in unstructured environments where a dynamic and reactive adaptation of motions is often required. This paper presents a novel framework that generates adaptive behaviors by modeling skills as concurrent motion primitives activated dynamically when conditions trigger. The approach exploits the additive property of motion generators to superpose multiple contributions. We demonstrate the applicability on a real assembly use-case and discuss the gained benefits. (Less)
Abstract (Swedish)
Task level programming based on skills has often been proposed as a mean to decrease programming complexity of industrial robots. Several models are based on encapsulating complex motions into self-contained primitive blocks. A semantic skill is then defined as a deterministic sequence of these primitives. A major limitation is that existing frameworks do not support the coordination of concurrent motion primitives with possible interference. This decreases their reusability and scalability in unstructured environments where a dynamic and reactive adaptation of motions is often required. This paper presents a novel framework that generates adaptive behaviors by modeling skills as concurrent motion primitives activated dynamically when... (More)
Task level programming based on skills has often been proposed as a mean to decrease programming complexity of industrial robots. Several models are based on encapsulating complex motions into self-contained primitive blocks. A semantic skill is then defined as a deterministic sequence of these primitives. A major limitation is that existing frameworks do not support the coordination of concurrent motion primitives with possible interference. This decreases their reusability and scalability in unstructured environments where a dynamic and reactive adaptation of motions is often required. This paper presents a novel framework that generates adaptive behaviors by modeling skills as concurrent motion primitives activated dynamically when conditions trigger. The approach exploits the additive property of motion generators to superpose multiple contributions. We demonstrate the applicability on a real assembly use-case and discuss the gained benefits. (Less)
Please use this url to cite or link to this publication:
author
; ; ; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
assembly, behavior trees, industrial robots, motion generators, reactive system, skills
host publication
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
pages
8 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:85063007454
ISBN
978-1-5386-8095-7
978-1-5386-8094-0
DOI
10.1109/IROS.2018.8594319
language
English
LU publication?
no
id
61a44b65-2beb-4614-8b99-d76cc472b094
date added to LUP
2019-05-16 21:20:11
date last changed
2021-04-13 05:16:37
@inproceedings{61a44b65-2beb-4614-8b99-d76cc472b094,
  abstract     = {Task level programming based on skills has often been proposed as a mean to decrease programming complexity of industrial robots. Several models are based on encapsulating complex motions into self-contained primitive blocks. A semantic skill is then defined as a deterministic sequence of these primitives. A major limitation is that existing frameworks do not support the coordination of concurrent motion primitives with possible interference. This decreases their reusability and scalability in unstructured environments where a dynamic and reactive adaptation of motions is often required. This paper presents a novel framework that generates adaptive behaviors by modeling skills as concurrent motion primitives activated dynamically when conditions trigger. The approach exploits the additive property of motion generators to superpose multiple contributions. We demonstrate the applicability on a real assembly use-case and discuss the gained benefits.},
  author       = {Rovida, Francesco and Wuthier, David and Grossmann, Bjarne and Fumagalli, Matteo and Krüger, Volker},
  booktitle    = {2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018},
  isbn         = {978-1-5386-8095-7},
  language     = {eng},
  month        = {12},
  pages        = {5964--5971},
  publisher    = {IEEE - Institute of Electrical and Electronics Engineers Inc.},
  title        = {Motion Generators Combined with Behavior Trees: A Novel Approach to Skill Modelling},
  url          = {http://dx.doi.org/10.1109/IROS.2018.8594319},
  doi          = {10.1109/IROS.2018.8594319},
  year         = {2018},
}