Neural Controller for a Mobile Robot in a Nonstationary Enviornment

Recently it has been introduced a neural controller for a mobile robot that learns both forward and inverse odometry of a differential-drive robot through an unsupervised learning-by-doing cycle. This article introduces an obstacle avoidance module that is integrated into the neural controller. This module makes use of sensory information to determine at each instant a desired angle and distance that causes the robot to navigate around obstacles on the way to a final target. Obstacle avoidance is performed in a reactive manner by representing the objects and target in the robot's environment as Gaussian functions. However, the influence of the Gaussians is modulated dynamically on the basis of the robot's behavior in a way that avoids problems with local minima. The proposed module enables the robot to operate successfully with different obstacle configurations, such as corridors, mazes, doors and even concave obstacles.Air Force Office of Scientific Research (F49620-92-J-0499

“Arisco” un robot social con capacidad de interaccion, motivacion y aprendizaje

ResumenEn este artículo se describe la arquitectura del robot social “Arisco” con especial atención a su sistemas de interacción, motivación, planificación y aprendizaje. Arisco es una cabeza mecatrónica con capacidad de interacción y que incluye: gran expresividad mediante gesticulación, reconocimiento y síntesis de voz, seguimiento visual, extracción de información de internet, y sistema de aprendizaje y motivación

“ARISCO” a social robot with interaction capability, motivation and learning

[EN] In this paper, the architecture of “Arisco” social robot is described, with particular stress on its interaction, motivation, planning and learning system. Arisco is a mechatronic head with interaction capacity, which includes: large expressivity through gestures, voice recognition and synthesis, visual tracking, information retrieval from Internet, and a learning and motivation system.[ES] En este artículo se describe la arquitectura del robot social “Arisco” con especial atención a su sistemas de interacción, motivación, planificación y aprendizaje. Arisco es una cabeza mecatrónica con capacidad de interacción y que incluye: gran expresividad mediante gesticulación, reconocimiento y síntesis de voz, seguimiento visual, extracción de información de internet, y sistema de aprendizaje y motivación.Los trabajos de investigación realizados han sido financiados por el Ministerio de Ciencia y Tecnología (proyectos DPI2002-04377-C02-01, DPI2005-06911), y por la Junta de Castilla y León (programas de ayudas a Proyectos de Investigación).Domínguez, S.; Zalama, E.; García-Bermejo, J. (2008). “ARISCO” un robot social con capacidad de interacción, motivación y aprendizaje. Revista Iberoamericana de Automática e Informática industrial. 5(2):69-78. http://hdl.handle.net/10251/145707OJS69785

Neural Network For The Behavioral Navigation Of A Mobile Robot

. This paper describes a neural network model for the reactive navigation of a mobile robot. The system defines a series of reactive behaviors: stop, avoid, stroll, wall following, etc. depending on the information obtained from a set of proximity sensors distributed in the periphery of the robot. Reinforcing learning permits the adaptative navigation of the robot. Key Words. mobile robots; obstacle avoidance; learning control; neural networks; robot navigation; reactive behavior 1. INTRODUCTION Mobile robotics has been one of the most interesting fields for the researchers in the last years. Many researchers of different sciences have been interested in mobile robotics from different points of view: Control Engineering, Mechanical Engineering, Physics, Computer, Mathematics, even psychologists and ethiologists. Traditional robotics techniques are not directly applicable to mobile robotics, because in most cases the work environment is not well defined and it is difficult to determine..

Neural Controller For A Mobile Robot In A Nonstationary Environment

. Recently it has been introduced a neural controller for a mobile robot that learns both forward and inverse odometry of a differential-drive robot through an unsupervised learning-bydoing cycle. This article introduces an obstacle avoidance module that is integrated into the neural controller. This module makes use of sensory information to determine at each instant a desired angle and distance that causes the robot to navigate around obstacles on the way to a final target. Obstacle avoidance is performed in a reactive manner by representing the objects and target in the robot's environment as Gaussian functions. However, the influence of the Gaussians is modulated dynamically on the basis of the robot's behavior in a way that avoids problems with local minima. The proposed module enables the robot to operate successfully with different obstacle configurations, such as corridors, mazes, doors and even concave obstacles. Key Words. mobile robots; obstacle avoidance; learning control;..

Automatic And Flexible Transport System Based On Mobile Robots

. This paper describes the development of a mobile robot based on a fork lift truck. This system has been tested in a ham industry and permits to integrate the previous transport system of the factory. In this way, the transport and storage can be done using fork lift trucks, mobile robots or both, and all of them controlled by a planning program. The planner obtains information from an automaton about the kind of product that arrives via the production line, and based on the state of the plant, it decides which robot is going to be used and where it has to transport the load. Localization of the mobile robot in the plant is performed by two infrared sensors and a laser telemeter on board, that is able to give angular and distance measures to beacons located in the plant. Key Words. mobile robots; autonomous vehicles; transportation; path planning; position location 1. INTRODUCTION The quick evolution of the Information Technology, Control, Computers and Comunications, support the ide..