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Avi Rosenfeld, Gal A. Kaminka,
Sarit Kraus, and Onn Shehory. A Study of Mechanisms for Improving Robotic
Group Performance. Artificial Intelligence, 172(6--7):633–655, 2008.
Many collaborative multi-robot application domains have limited areas ofoperation that cause spatial conflicts between robotic teammates.These spatial conflicts can cause the team's productivity todrop with the addition of robots. This phenomenon is impacted by thecoordination methods used by the team-members, as different coordinationmethods yield radically different productivity results. However,selecting the best coordination method to be used by teammatesis a formidable task. This paper presents techniques for creating adaptivecoordination methods, to address this challenge.We first present a combined coordination cost measure, CCC, forquantifying the cost of group interactions. Our measure is usefulfor facilitating comparison between coordination methods, even whenmultiple cost factors are considered. We consistently find that asCCC values grow, group productivity falls.Using the CCC, we create adaptive coordination techniquesthat are able to dynamically adjust the efforts spent oncoordination to match the number of perceived coordination conflictsin a group. We present two adaptation heuristics that are completelydistributed and require no communication between robots. Using these heuristics, robotsindependently estimate of their combined coordinationcost (CCC), and adjust their coordination methods tominimize it, and increase group productivity. We used a simulated robots toperform thousands of experiment trials, to demonstrate the efficacy ofour approach. We show that using adaptive coordination methods creates astatistically significant improvement in productivity over staticmethods, regardless of the group size.
The article's official web page is at: http://dx.doi.org/10.1016/j.artint.2007.09.008.
@Article{aij07avi,
author = {Avi Rosenfeld and Gal A. Kaminka and Sarit Kraus and Onn Shehory},
title = {A Study of Mechanisms for Improving Robotic Group Performance},
journal = AIJ,
year = {2008},
volume = {172},
number = {6--7},
pages = {633--655},
OPTnote = {},
abstract = {Many collaborative multi-robot application domains have limited areas of
operation that cause spatial conflicts between robotic teammates.
These spatial conflicts can cause the team's productivity to
drop with the addition of robots. This phenomenon is impacted by the
coordination methods used by the team-members, as different coordination
methods yield radically different productivity results. However,
selecting the best coordination method to be used by teammates
is a formidable task. This paper presents techniques for creating adaptive
coordination methods, to address this challenge.
We first present a combined coordination cost measure, CCC, for
quantifying the cost of group interactions. Our measure is useful
for facilitating comparison between coordination methods, even when
multiple cost factors are considered. We consistently find that as
CCC values grow, group productivity falls.
Using the CCC, we create adaptive coordination techniques
that are able to dynamically adjust the efforts spent on
coordination to match the number of perceived coordination conflicts
in a group. We present two adaptation heuristics that are completely
distributed and require no communication between robots. Using these heuristics, robots
independently estimate of their combined coordination
cost (CCC), and adjust their coordination methods to
minimize it, and increase group productivity. We used a simulated robots to
perform thousands of experiment trials, to demonstrate the efficacy of
our approach. We show that using adaptive coordination methods creates a
statistically significant improvement in productivity over static
methods, regardless of the group size.},
wwwnote = {},
}
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