Abstract | In this paper we describe the evolution of a discrete-time recurrent neural network to control a real mobile robot. In all our experiments the evolutionary procedure is carried out entirely on the physical robot without human intervention. We showthat the autonomous development of a set of behaviors for locating a battery charger and periodically returning to it can be achieved by lifting constraints in the design of the robot/environment interactions that were employed in a preliminary experiment. The emergent homing behavior is based on the autonomous development ofaninternal neural topographic map (which is not pre-designed) that allows the robot to choose the appropriate trajectory as function of location and remaining energy.

... the last 5 years. We explain and justify our distinctive approaches to (artificial) evolution, and to the nature of robot control systems that are evolved. Results are presented from research with evolved controllers for autonomous mobile robots; simulated robots, coevolved animats, real robots with software controllers, and a real robot with a controller directly evolved in hardware.

. Artificial evolution can operate upon reconfigurable electronic circuits to produce efficient and powerful control systems for autonomous mobile robots. Evolving physical hardware instead of control systems simulated in software results in more than just a raw speed increase: it is possible to exploit the physical properties of the implementation (such as the semiconductor physics of integrated circuits) to obtain control circuits of unprecedented power. The space of these evolvable circuits is far larger than the space of solutions in which a human designer works, because to make design tractable, a more abstract view than that of detailed physics must be adopted. To allow circuits to be designed at this abstract level, constraints are applied to the design that limit how the natural dynamical behaviour of the components is reflected in the overall behaviour of the system. This paper reasons that these constraints can be removed when using artificial evolution, releasing huge potent...

This paper presents an analysis of an artificially evolved dynamical network-based control system for a simulated autonomous mobile robot engaged in simple visually guided tasks.

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From perception to action and from action to perception, all elements of an autonomous agent are interdependent and need to be strongly coherent. The final behavior of the agent is the result of the global activity of this loop and every weakeness or incoherence of a single element has strong consequences on the performances of the agent. We think that, for the purpose of building autonomous robots, all these elements need to be developed together in continuous interaction with the environment. We describe the implementation of a possible solution (artificial neural networks and genetic algorithms) on a real mobile robot through a set of three different experiments. We focus our attention on three different aspects of the control structure: perception, internal representation and action. In all the experiments these aspects are not considered as single processing elements, but as part of an agent. For every experiment, the advantages and disadvantages of this approach are presented and...

This paper successively describes the works of Boers & Kuiper, Gruau, Cangelosi et al., Vaario, Dellaert & Beer, and Sims, which all evolve the developmental program of an artificial nervous system. The potentialities of these approaches for automatically devising a control architecture linking the perceptions and the actions of an animat are then discussed, together with their possible contributions to the fundamental issue of assessing the adaptive values of development, learning and evolution.

This article describes past and current research efforts in evolutionary robotics that have been carried out at the AnimatLab, Paris. Such approaches entail using an artificial selection process to automatically generate developmental programs for neural networks that control rolling, walking, swimming and flying animats or robots. Basically, they complement the underlying evolutionary process with a developmental procedure – in order hopefully to reduce the size of the genotypic space that is explored – and they occasionally call on an incremental approach, in order to capitalize upon solutions to simpler problems so as to devise solutions to more complex problems. This article successively outlines the historical background of our research, the evolutionary paradigm on which it relies, and the various results obtained so far. It also discusses the potentialities and limitations of the approach and indicates directions for future work.

. This paper introduces a new type of artificial nervous system and shows that it is possible to use evolutionary computing techniques to find robot controllers based on them. The controllers are built from networks inspired by the modulatory effects of freely diffusing gases, especially nitric oxide, in real neuronal networks. Using Jakobi's radical minimal simulations, successful behaviours have been consistently evolved in far fewer evaluations than were needed when using more conventional connectionist style networks. Indeed the reduction is by a factor of roughly one order of magnitude. 1 Introduction In evolutionary robotics, the predominant class of systems for generating behaviours is that of artificial neural networks (ANNs). These networks can be envisaged as simple nodes connected together by directional wires along which signals flow. The nodes perform an input output mapping that is usually some sort of sigmoid function [7]. Occasionally a simple differential equation is ...

Recently there have been a number of proposals for the use of artificial evolution as a radically new approach to the development of control systems for autonomous robots. This paper explains the artificial evolution approach, using work at Sussex to illustrate it. The paper revolves around a case study on the concurrent evolution of control networks and visual sensor morphologies for a mobile robot. Wider intellectual evolutionary simulations as a new potentially useful tool in theoretical biology.