Extended behavior trees for quick definition of flexible robotic tasks
Rovida, Francesco ; Grossmann, Bjarne and Kruger, Volker LU
(2017)
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017)
p.6793-6800
- Abstract
- he requirement of flexibility in the modern industries demands robots that can be efficiently and quickly adapted to different tasks. A way to achieve such a flexible programming paradigm is to instruct robots with task goals and leave planning algorithms to deduct the correct sequence of actions to use in the specific context. A common approach is to connect the skills that realize a semantically defined operation in the planning domain - such as picking or placing an object - to specific executable functions. As a result the skills are treated as independent components, which results into suboptimal execution. In this paper we present an approach where the execution procedures and the planning domain are specified at the same time using... (More)
- he requirement of flexibility in the modern industries demands robots that can be efficiently and quickly adapted to different tasks. A way to achieve such a flexible programming paradigm is to instruct robots with task goals and leave planning algorithms to deduct the correct sequence of actions to use in the specific context. A common approach is to connect the skills that realize a semantically defined operation in the planning domain - such as picking or placing an object - to specific executable functions. As a result the skills are treated as independent components, which results into suboptimal execution. In this paper we present an approach where the execution procedures and the planning domain are specified at the same time using solely extended Behavior Trees (eBT), a model formalized and discussed in this paper. At run-time, the robot can use the more abstract skills to plan a sequence using a PDDL planner, expand the sequence into a hierarchical tree, and re-organize it to optimize the time of execution and the use of resources. The optimization is demonstrated on a kitting operation in both simulation and lab environment, showing up to 20% save in the final execution time. (Less)
- Abstract (Swedish)
- The requirement of flexibility in the modern industries demands robots that can be efficiently and quickly adapted to different tasks. A way to achieve such a flexible programming paradigm is to instruct robots with task goals and leave planning algorithms to deduct the correct sequence of actions to use in the specific context. A common approach is to connect the skills that realize a semantically defined operation in the planning domain - such as picking or placing an object - to specific executable functions. As a result the skills are treated as independent components, which results into suboptimal execution. In this paper we present an approach where the execution procedures and the planning domain are specified at the same time using... (More)
- The requirement of flexibility in the modern industries demands robots that can be efficiently and quickly adapted to different tasks. A way to achieve such a flexible programming paradigm is to instruct robots with task goals and leave planning algorithms to deduct the correct sequence of actions to use in the specific context. A common approach is to connect the skills that realize a semantically defined operation in the planning domain - such as picking or placing an object - to specific executable functions. As a result the skills are treated as independent components, which results into suboptimal execution. In this paper we present an approach where the execution procedures and the planning domain are specified at the same time using solely extended Behavior Trees (eBT), a model formalized and discussed in this paper. At run-time, the robot can use the more abstract skills to plan a sequence using a PDDL planner, expand the sequence into a hierarchical tree, and re-organize it to optimize the time of execution and the use of resources. The optimization is demonstrated on a kitting operation in both simulation and lab environment, showing up to 20% save in the final execution time. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/f90b3bb9-e82a-4856-92ed-40ce3b490fb6
- author
- Rovida, Francesco
; Grossmann, Bjarne
and Kruger, Volker
LU
- publishing date
- 2017-12-13
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- autonomous robots, behavior trees, hierarchical task networks, planning, skills
- host publication
- 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- pages
- 8 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017)
- conference location
- Vancouver, Canada
- conference dates
- 2017-09-24 - 2017-09-28
- external identifiers
-
- scopus:85041949828
- ISBN
- 978-1-5386-2682-5
- 978-1-5386-2683-2
- DOI
- 10.1109/IROS.2017.8206598
- language
- English
- LU publication?
- no
- id
- f90b3bb9-e82a-4856-92ed-40ce3b490fb6
- date added to LUP
- 2019-05-16 21:19:42
- date last changed
- 2024-01-30 18:35:01
@inproceedings{f90b3bb9-e82a-4856-92ed-40ce3b490fb6,
abstract = {{he requirement of flexibility in the modern industries demands robots that can be efficiently and quickly adapted to different tasks. A way to achieve such a flexible programming paradigm is to instruct robots with task goals and leave planning algorithms to deduct the correct sequence of actions to use in the specific context. A common approach is to connect the skills that realize a semantically defined operation in the planning domain - such as picking or placing an object - to specific executable functions. As a result the skills are treated as independent components, which results into suboptimal execution. In this paper we present an approach where the execution procedures and the planning domain are specified at the same time using solely extended Behavior Trees (eBT), a model formalized and discussed in this paper. At run-time, the robot can use the more abstract skills to plan a sequence using a PDDL planner, expand the sequence into a hierarchical tree, and re-organize it to optimize the time of execution and the use of resources. The optimization is demonstrated on a kitting operation in both simulation and lab environment, showing up to 20% save in the final execution time.}},
author = {{Rovida, Francesco and Grossmann, Bjarne and Kruger, Volker}},
booktitle = {{2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}},
isbn = {{978-1-5386-2682-5}},
keywords = {{autonomous robots; behavior trees; hierarchical task networks; planning; skills}},
language = {{eng}},
month = {{12}},
pages = {{6793--6800}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Extended behavior trees for quick definition of flexible robotic tasks}},
url = {{http://dx.doi.org/10.1109/IROS.2017.8206598}},
doi = {{10.1109/IROS.2017.8206598}},
year = {{2017}},
}