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Towards adaptive multi-robot systems: self-organization and self-adaptation

Published online by Cambridge University Press:  04 October 2018

Christopher-Eyk Hrabia ,
Marco Lützenberger  and
Sahin Albayrak
Christopher-Eyk Hrabia
Affiliation:
DAI-Labor, Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, Ernst-Reuter-Platz 7, 10587 Berlin, Germany; e-mail: [email protected], [email protected], [email protected]
Marco Lützenberger
Affiliation:
DAI-Labor, Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, Ernst-Reuter-Platz 7, 10587 Berlin, Germany; e-mail: [email protected], [email protected], [email protected]
Sahin Albayrak
Affiliation:
DAI-Labor, Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, Ernst-Reuter-Platz 7, 10587 Berlin, Germany; e-mail: [email protected], [email protected], [email protected]
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Abstract

The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible.

Type
Principles and Practice of Multi-Agent Systems
Information
The Knowledge Engineering Review , Volume 33 , 2018 , e16
Copyright
© Cambridge University Press, 2018 

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