Reproduction de : Thèse de doctorat : informatique : Grenoble, INPG : 1987. Reproduction de : Thèse de doctorat : informatique : Grenoble 1 : 1987. / Titre provenant de l'écran-titre. Bibliogr. p.[105]-109.

Reproduction de : Thèse d'Etat : sciences : Grenoble, INPG : 1987. / Titre provenant de l'écran-titre. Bibliogr. p. 265-279.

In this thesis, we use evolutionary robotics techniques to automatically design and synthesise<p>behaviour for groups of simulated and real robots. Our contribution will be on<p>the design of non-trivial individual and collective behaviour; decisions about solitary or<p>social behaviour will be temporal and they will be interdependent with communicative<p>acts. In particular, we study time-based decision-making in a social context: how the<p>experiences of robots unfold in time and how these experiences influence their interaction<p>with the rest of the group. We propose three experiments based on non-trivial real-world<p>cooperative scenarios. First, we study social cooperative categorisation; signalling and<p>communication evolve in a task where the cooperation among robots is not a priori required.<p>The communication and categorisation skills of the robots are co-evolved from<p>scratch, and the emerging time-dependent individual and social behaviour are successfully<p>tested on real robots. Second, we show on real hardware evidence of the success of evolved<p>neuro-controllers when controlling two autonomous robots that have to grip each other<p>(autonomously self-assemble). Our experiment constitutes the first fully evolved approach<p>on such a task that requires sophisticated and fine sensory-motor coordination, and it<p>highlights the minimal conditions to achieve assembly in autonomous robots by reducing<p>the assumptions a priori made by the experimenter to a functional minimum. Third, we<p>present the first work in the literature to deal with the design of homogeneous control<p>mechanisms for morphologically heterogeneous robots, that is, robots that do not share<p>the same hardware characteristics. We show how artificial evolution designs individual<p>behaviours and communication protocols that allow the cooperation between robots of<p>different types, by using dynamical neural networks that specialise on-line, depending on<p>the nature of the morphology of each robot. The experiments briefly described above<p>contribute to the advancement of the state of the art in evolving neuro-controllers for<p>collective robotics both from an application-oriented, engineering point of view, as well as<p>from a more theoretical point of view. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished