Computation of a collision-free path for a movable object among obstacles is an important problem in the fields of robotics. The simplest version of motion planning consists in generating a collisionfree path for a movable object among known and static obstacles. In this paper, we introduce a two stages evolutionary algorithm. The first stage is designed to compute a collission-free path in a k...

The pitfalls of naive robot simulations have been recognised for areas such as evolutionary robotics. It has been suggested that carefully validated ispell slides.tex simulations with a proper treatment of noise may overcome these problems. This paper reports the results of experiments intended to test some of these claims. A simulation was constructed of a two-wheeled Khepera robot with IR and...

In this article we present two algorithms, for reducing the effects of control-space quantisation errors on a Khepera mobile robot. Specifically, we consider that control-space quantisation is present, when there is only a finite set of available robot wheels velocities. Thus the velocities of the robot wheels can not be chosen from any point in a continuous set. The first algorithm can be used...

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...

Tangible agent (TA) is a new medium that communicates the senses of sight, hearing, touch, smell, and taste of human to computer. Intelligent behavior is a key property to realize the agent because it must interact with human to grasp all the information. We have adopted behavior-based approach for high-level hehaviors such as navigation of office, conversation with human, and cleaning of room....

In this paper we present the development of a method that combines the evolutionary robotics approach with action selection. A collection task is set in an arena where a Khepera robot has to collect cylinders that simulate food. Furthermore, two basic motivations, labeled as ‘fear’ and ‘hunger’, both affect the selection of the behavioral repertoire. In this paper we propose an initial evolutio...

Is it more eecient to use one or several robots? Will the performance of a group of robots working in a collaborative task be enhanced if the robots can communicate with one another? What learning abilities should the robot(s) be provided with for adapting to a continuously changing environment? We address these three issues in a speciic task, namely learning the topography of an environment wh...

We present a neural network that learns to control approach and avoidance behaviors in a mobile robot based on a form of animal learning known as operant conditioning. Learning, which requires no supervision, takes place as the robot moves around an environment cluttered with obstacles and light sources. The neural network requires no knowledge of the geometry of the robot or of the quality, nu...

The navigation problem involves how to reach a goal avoiding obstacles in dynamic environments. This problem can be faced considering reactions and sequences of actions. Classifier Systems (CS) have proven their ability of continuos learning, however they have some problems in reactive systems. A modified CS, Reactive Classifier System (RCS), is proposed to overcome those problems. Two special ...

In many cases, a real robot application requires the navigation in dynamic environments. The navigation problem involves two main tasks: to avoid obstacles and to reach a goal. Generally, this problem could be faced considering reactions and sequences of actions. For solving the navigation problem a complete controller, including actions and reactions, is needed. Machine learning techniques has...