In this paper an evolution strategy (ES) is introduced, to learn reactive behaviour in autonomous robots. An ES is used to learn high-performance reactive behaviour for navigation and collisions avoidance. The learned behaviour is able to solve the problem in a dynamic environment; so, the learning process has proven the ability to obtain generalised behaviours. The robot starts without informa...

This project investigates the evolution in simulation of robot controllers capable of performing a hard task, playing football, in the real world. It is argued that for any reasonably interesting task, robot controllers are too di cult to design, and that an evolutionary methodology must be employed. It is further argued that evolution in the real world is too slow for such a task, and that evo...

A computer language is a very general form of representing and specifying an autonomous agent's behavior. The task of planning feasible actions could then simply be reduced to an instance of automatic programming. We have evaluated the use of an evolutionary technique for automatic programming called Genetic Programming (GP) to directly control a miniature robot. To our knowledge, this is the r...

Genetic Algorithms (GAs) have demonstrated to be effective procedures for solving multicriterion optimization problems. These algorithms mimic models of natural evolution and have the ability to adaptively search large spaces in near-optimal ways. One direct application of this intelligent technique is in the area of evolutionary robotics, where GAs are typically used for designing behavioral c...

In this paper, we study the path planning for khepera II mobile robot in an unknown environment. The well known heuristic D* lite algorithm is implemented to make the mobile robot navigate through static obstacles and find the shortest path from an initial position to a target position by avoiding the obstacles. and to perform efficient re-planning during exploration. The proposed path finding ...

The work presents the development of a complex navigational behavior combining some simple fuzzy behaviors for a mobile agricultural robot (MAR). Fuzzy rules are used to compose and coordinate the simple behaviors and an experimental robotic platform based on Khepera mini-robot is proposed to simulate and support the researches on behavior based architecture for agricultural robots. The mini-ro...

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 ...

Evolution-in-materio is a method that uses artificial evolution to exploit properties of materials to solve computational problems without requiring a detailed understanding of such properties. In this paper, we show that using a purpose-built hardware platform called Mecobo, it is possible to evolve voltages and signals applied to physical materials to solve two computational problems: classif...

The goal of this work is to augment reinforcement learning techniques for autonomous robot navigation with a state space encoding more representative of the actual state of the robot in its environment, than available from direct sensor readings. A second goal is to demonstrate the approach in a real-world setting, using the microrobot Khepera (K-Team, Lausanne, Switzerland). The choice of stat...

In this paper we demonstrate an eecient method which divides a control task into smaller sub{tasks. We use a Genetic Programming system that rst learns the sub-tasks and then evolves a higher{level action selection strategy for deciding which of the evolved lower{level algorithms should be in control. The Swiss miniature robot Khepera is employed as the experimental platform. Results are presen...