On the evolution of autonomous decision-making and communication in collective robotics

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