Abstract We study task partitioning in the context of
swarm robotics. Task partitioning is the decomposition
of a task into subtasks that can be tackled by
different workers. We focus on the case in which a task
is partitioned into a sequence of subtasks that must be
executed in a certain order. This implies that the
subtasks must interface with each other, and that the
output of a subtask is used as input for the subtask
that follows. A distinction can be made between task
partitioning with direct transfer and with indirect
transfer. We focus our study on the first case: The
output of a subtask is directly transferred from an
individual working on that subtask to an individual
working on the subtask that follows. As a test bed for
our study, we use a swarm of robots performing
foraging. The robots have to harvest objects from a
source, situated in an unknown location, and transport
them to a home location. When a robot finds the source,
it memorizes its position and uses dead reckoning to
return there. Dead reckoning is appealing in robotics,
since it is a cheap localization method and it does not
require any additional external infrastructure.
However, dead reckoning leads to errors that grow in
time if not corrected periodically. We compare a
foraging strategy that does not make use of task
partitioning with one that does. We show that
cooperation through task partitioning can be used to
limit the effect of dead reckoning errors. This results
in improved capability of locating the object source
and in increased performance of the swarm. We use the
implemented system as a test bed to study benefits and
costs of task partitioning with direct transfer. We
implement the system with real robots, demonstrating
the feasibility of our approach in a foraging
scenario.
%0 Journal Article
%1 pini-task-partitioning-swarm-2014
%A Pini, Giovanni
%A Brutschy, Arne
%A Scheidler, Alexander
%A Dorigo, Marco
%A Birattari, Mauro
%D 2014
%I MIT Press - Journals
%J Artificial Life
%K alife robot swarm
%N 3
%P 291--317
%R 10.1162/artl_a_00132
%T Task Partitioning in a Robot Swarm: Object Retrieval
as a Sequence of Subtasks with Direct Object Transfer
%U http://dx.doi.org/10.1162/ARTL_a_00132
%V 20
%X Abstract We study task partitioning in the context of
swarm robotics. Task partitioning is the decomposition
of a task into subtasks that can be tackled by
different workers. We focus on the case in which a task
is partitioned into a sequence of subtasks that must be
executed in a certain order. This implies that the
subtasks must interface with each other, and that the
output of a subtask is used as input for the subtask
that follows. A distinction can be made between task
partitioning with direct transfer and with indirect
transfer. We focus our study on the first case: The
output of a subtask is directly transferred from an
individual working on that subtask to an individual
working on the subtask that follows. As a test bed for
our study, we use a swarm of robots performing
foraging. The robots have to harvest objects from a
source, situated in an unknown location, and transport
them to a home location. When a robot finds the source,
it memorizes its position and uses dead reckoning to
return there. Dead reckoning is appealing in robotics,
since it is a cheap localization method and it does not
require any additional external infrastructure.
However, dead reckoning leads to errors that grow in
time if not corrected periodically. We compare a
foraging strategy that does not make use of task
partitioning with one that does. We show that
cooperation through task partitioning can be used to
limit the effect of dead reckoning errors. This results
in improved capability of locating the object source
and in increased performance of the swarm. We use the
implemented system as a test bed to study benefits and
costs of task partitioning with direct transfer. We
implement the system with real robots, demonstrating
the feasibility of our approach in a foraging
scenario.
@article{pini-task-partitioning-swarm-2014,
abstract = {Abstract We study task partitioning in the context of
swarm robotics. Task partitioning is the decomposition
of a task into subtasks that can be tackled by
different workers. We focus on the case in which a task
is partitioned into a sequence of subtasks that must be
executed in a certain order. This implies that the
subtasks must interface with each other, and that the
output of a subtask is used as input for the subtask
that follows. A distinction can be made between task
partitioning with direct transfer and with indirect
transfer. We focus our study on the first case: The
output of a subtask is directly transferred from an
individual working on that subtask to an individual
working on the subtask that follows. As a test bed for
our study, we use a swarm of robots performing
foraging. The robots have to harvest objects from a
source, situated in an unknown location, and transport
them to a home location. When a robot finds the source,
it memorizes its position and uses dead reckoning to
return there. Dead reckoning is appealing in robotics,
since it is a cheap localization method and it does not
require any additional external infrastructure.
However, dead reckoning leads to errors that grow in
time if not corrected periodically. We compare a
foraging strategy that does not make use of task
partitioning with one that does. We show that
cooperation through task partitioning can be used to
limit the effect of dead reckoning errors. This results
in improved capability of locating the object source
and in increased performance of the swarm. We use the
implemented system as a test bed to study benefits and
costs of task partitioning with direct transfer. We
implement the system with real robots, demonstrating
the feasibility of our approach in a foraging
scenario.},
added-at = {2015-02-02T12:03:03.000+0100},
author = {Pini, Giovanni and Brutschy, Arne and Scheidler, Alexander and Dorigo, Marco and Birattari, Mauro},
biburl = {https://www.bibsonomy.org/bibtex/29a8df42977db23e171a41d536580e464/mhwombat},
doi = {10.1162/artl_a_00132},
interhash = {156fce09c1654253bd1dc387ff6dc491},
intrahash = {9a8df42977db23e171a41d536580e464},
journal = {Artificial Life},
keywords = {alife robot swarm},
month = jul,
number = 3,
pages = {291--317},
publisher = {{MIT} Press - Journals},
timestamp = {2016-07-12T19:25:30.000+0200},
title = {Task Partitioning in a Robot Swarm: Object Retrieval
as a Sequence of Subtasks with Direct Object Transfer},
url = {http://dx.doi.org/10.1162/ARTL_a_00132},
volume = 20,
year = 2014
}