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Ph.D de

Ph.D
Group : Learning and Optimization

Environment-driven Distributed Evolutionary Adaptation for Collective Robotic Systems

Starts on 01/10/2009
Advisor : BREDECHE, Nicolas

Funding : A
Affiliation : Université Paris-Sud
Laboratory : LRI AO

Defended on 01/03/2013, committee :
Reviewers:
A.E. Eiben - VU Amsterdam, Netherlands
Guillaume Beslon - LIRIS/CNRS/INRIA, INSA de Lyon

Examiners:
Stéphane Doncieux - ISIR/CNRS, UPMC
Marc Schoenauer - INRIA Saclay IdF
Philippe Tarroux - LIMSI/CNRS, Université Paris-Sud

Advisor:
Nicolas Bredèche - ISIR/CNRS, UPMC

Research activities :

Abstract :
This thesis describes some of the work done in the context of the Symbrion project. This project targets the realization of complex tasks which require the cooperation of multiple robots within robotic swarms (at least 100 robots operating together). Among the issues studied by the project are the self-assembly of robots to form complex structures and the self-organization of large number of robots toward the realization of a common task. Subjects of interests are thus modular self-adaptive robots with both strong coordination properties, and swarm-level cooperation.

The challenge faced by this project is that robots are used in open environments which remain unknown until their deployment. Since operational conditions can’t be predicted beforehand, on-line learning algorithms must be used to design behaviors. In the use of large groups of robots, multiple considerations have to be taken into account such as reduced communication abilities, small memory storage, small computational power. In this context, on-line learning algorithms must be distributed among robots as central control is not an option.

Within this context, the thesis address the problem of maintaining swarm integrity (i.e. to maximize the number of "active" robots). We introduce and define the problem of environment-driven distributed evolutionary adaptation, and present an algorithm to solve this problem. This algorithm has been validated both in simulation and with real robots, and several case studies have been investigated. In particular, we studied the dynamics of the algorithm under various environmental conditions. In particular: evolutionary dynamics and convergence to stable behaviors, robustness to environmental changes and evolution of altruistic behavior in adversarial environment.

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