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Conference object . 2018 . Peer-reviewed
License: CC BY NC ND
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Conference object . Other literature type . Article . 2018
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interleaving hierarchical task planning and motion constraint testing for dual arm manipulation

Authors: Suárez Hernández, Alejandro; Alenyà, Guillem; Torras, Carme;

interleaving hierarchical task planning and motion constraint testing for dual arm manipulation

Abstract

In recent years the topic of combining motion and symbolic planning to perform complex tasks in the field of robotics has received a lot of attention. The underlying idea is to have access at once to the reasoning capabilities of a task planner and to the ability of the motion planner to verify that the plan is feasible from a physical and geometrical point of view. The present work describes a framework to perform manipulation tasks that require the use of two robotic manipulators. To do so we employ a Hierarchical Task Network (HTN) planner interleaved with geometric constraint verification. In this framework we also consider observation actions and handle noisy perceptions from a probabilistic perspective. These ideas are put into practice by means of an experimental set-up in which two Barrett WAM robots have to cooperatively solve a geometric puzzle. Our findings provide further evidence that considering explicitly physical constraints during task planning, rather than deferring their validation to the moment of execution, is advantageous in terms of execution time and breadth of situations that can be handled.

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Peer Reviewed

Country
Spain
Keywords

Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial, Uncertainty, Shape, Cameras, Manipulators, Planning, Task analysis, :Informàtica::Intel·ligència artificial [Àrees temàtiques de la UPC], :Cybernetics::Artificial intelligence [Classificació INSPEC], Classificació INSPEC::Cybernetics::Artificial intelligence, Task analysis, Planning, Uncertainty, Shape, Manipulators, Cameras, planning (artificial intelligence)

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Top 10%
Average
Top 10%
Green