AbSTRACT This work is intended to give an overview of technologies, developed from an artificial intelligence standpoint, devised to face the different planning and control problems involved in trajectory generation for mobile robots. The purpose of this analysis is to give a current context to present the Evolutionary Robotics approach to the problem, which is now being considered as a feasibl...

This paper presents an emotion based process gener ating cognition through learning Two models are de scribed which investigates the possibilities of designing cognitive systems based on emotion In order to test these models through experiments we used a mobile robot simulator Webots modeling the real Khepera robot The rst proposed model is the simplest since it uses only positive and negative ...

A biologically inspired computational model of rodent representation–based (locale) navigation is presented. The model combines visual input in the form of realistic two dimensional grey-scale images and odometer signals to drive the firing of simulated place and head direction cells via Hebbian synapses. The space representation is built incrementally and on-line without any prior information ...

Due to their distributed architecture, artificial neural networks often show a graceful performance degradation to the loss of few units or connections. Living systems also display an additional source of fault-tolerance obtained through distributed processes of self-healing: defective components are actively regenerated. In this paper, we present results obtained with a model of development fo...

In this paper, a new coevolutive method, called Uniform Coevolution, is introduced to learn weights of a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collisions avoidance. The introduction of coevolutive over evolutionary strategies allows evolving the environment, to learn a general behavior able...

This study describes how complex goal-directed behavior can be obtained through adaptation processes in a hierarchically organized recurrent neural network using a genetic algorithm (GA). Our experiments, using a simulated Khepera robot, showed that different types of dynamic structures self-organize in the lower and higher levels of the network for the purpose of achieving complex navigation t...

This study describes how complex goal-directed behavior can evolve in a hierarchically organized recurrent neural network controlling a simulated Khepera robot. Different types of dynamic structures self-organize in the lower and higher levels of a network for the purpose of achieving complex navigation tasks. The parametric bifurcation structures that appear in the lower level explain the mech...

In the Neuro-robotics Lab of the Centre for Hybrid Intelligent Systems we have organised various challenging undergraduate student projects using Mindstorm, Khepera and PeopleBot robots. In this paper we will describe a particularly interesting undergraduate student project involving the introduction of intelligent behaviour onto a mini-robot platform. In the past robots were mainly hardcoded t...

In this paper a new coevolutive method, called Uniform Coevolution, is introduced, to learn weights of a neural network controller in autonomous robots. An evolutionary strategy is used to learn highperformance reactive behavior for navigation and collisions avoidance. The coevolutive method allows evolving the environment, to learn a general behavior able to solve the problem in different envi...