Title:
Decentralized Classification in Societies of Autonomous and Heterogenous Robots
Decentralized Classification in Societies of Autonomous and Heterogenous Robots
Author(s)
Martini, Simone
Fagiolini, Adriano
Zichittella, Giancarlo
Egerstedt, Magnus B.
Bicchi, Antonio
Fagiolini, Adriano
Zichittella, Giancarlo
Egerstedt, Magnus B.
Bicchi, Antonio
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Abstract
This paper addresses the classification problem
for a set of autonomous robots that interact with each other.
The objective is to classify agents that "behave" in "different
way", due to their own physical dynamics or to the interaction
protocol they are obeying to, as belonging to different "species".
This paper describes a technique that allows a decentralized
classification system to be built in a systematic way, once the
hybrid models describing the behavior of the different species
are given. This technique is based on a decentralized identification
mechanism, by which every agent classifies its neighbors
using only local information. By endowing every agent with
such a local classifier, the overall system is enhanced with the
ability to run behaviors involving individuals of the same species
as well as of different ones. The mechanism can also be used
to measure the level of cooperativeness of neighbors and to
discover possible intruders among them. General applicability
of the proposed solution is shown through examples of multi-agent systems from Biology and from Robotics.
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Date Issued
2011-05
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Proceedings