Evolution of Homing Navigation in a Real Mobile Robot

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 show that 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 of an internal neural topographic map (which is not pre-designed) that allows the robot to choose the appropriate trajectory as function of location and remaining energy

Visual servoing of a car-like vehicle - an application of omnidirectional vision

In this paper, we develop the switching controller presented by Lee et al. for the pose control of a car-like vehicle, to allow the use of an omnidirectional vision sensor. To this end we incorporate an extension to a hypothesis on the navigation behaviour of the desert ant, cataglyphis bicolor, which leads to a correspondence free landmark based vision technique. The method we present allows positioning to a learnt location based on feature bearing angle and range discrepancies between the robot's current view of the environment, and that at a learnt location. We present simulations and experimental results, the latter obtained using our outdoor mobile platform

Evolutionary online behaviour learning and adaptation in real robots

Online evolution of behavioural control on real robots is an open-ended approach to autonomous learning and adaptation: robots have the potential to automatically learn new tasks and to adapt to changes in environmental conditions, or to failures in sensors and/or actuators. However, studies have so far almost exclusively been carried out in simulation because evolution in real hardware has required several days or weeks to produce capable robots. In this article, we successfully evolve neural network-based controllers in real robotic hardware to solve two single-robot tasks and one collective robotics task. Controllers are evolved either from random solutions or from solutions pre-evolved in simulation. In all cases, capable solutions are found in a timely manner (1 h or less). Results show that more accurate simulations may lead to higher-performing controllers, and that completing the optimization process in real robots is meaningful, even if solutions found in simulation differ from solutions in reality. We furthermore demonstrate for the first time the adaptive capabilities of online evolution in real robotic hardware, including robots able to overcome faults injected in the motors of multiple units simultaneously, and to modify their behaviour in response to changes in the task requirements. We conclude by assessing the contribution of each algorithmic component on the performance of the underlying evolutionary algorithm.info:eu-repo/semantics/publishedVersio

Evolution strategies with q-Gaussian mutation for dynamic optimization problems

This article is posted here with permmission from IEEE - Copyright @ 2010 IEEEEvolution strategies with q-Gaussian mutation, which allows the self-adaptation of the mutation distribution shape, is proposed for dynamic optimization problems in this paper. In the proposed method, a real parameter q, which allows to smoothly control the shape of the mutation distribution, is encoded in the chromosome of the individuals and is allowed to evolve. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutation on four experiments generated from the simulation of evolutionary robots.This work was supported by FAPESP, Brazil, and by the Engineering and Physical Sciences Research Council(EP/E060722/1), UK

An Idiotypic Immune Network as a Short Term Learning Architecture for Mobile Robots

A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to solving mobile robot navigation problems is presented and tested in both real and simulated environments. The LTL consists of rapid simulations that use a Genetic Algorithm to derive diverse sets of behaviours. These sets are then transferred to an idiotypic Artificial Immune System (AIS), which forms the STL phase, and the system is said to be seeded. The combined LTL-STL approach is compared with using STL only, and with using a handdesigned controller. In addition, the STL phase is tested when the idiotypic mechanism is turned off. The results provide substantial evidence that the best option is the seeded idiotypic system, i.e. the architecture that merges LTL with an idiotypic AIS for the STL. They also show that structurally different environments can be used for the two phases without compromising transferabilityComment: 13 pages, 5 tables, 4 figures, 7th International Conference on Artificial Immune Systems (ICARIS2008), Phuket, Thailan

Control de robots móviles mediante visión omnidireccional utilizando la geometría de tres vistas

Este trabajo trata acerca del control visual de robot móviles. Dentro de este campo tan amplio de investigación existen dos elementos a los que prestaremos especial atención: la visión omnidireccional y los modelos geométricos multi-vista. Las cámaras omnidireccionales proporcionan información angular muy precisa, aunque presentan un grado de distorsión significativo en dirección radial. Su cualidad de poseer un amplio campo de visión hace que dichas cámaras sean apropiadas para tareas de navegación robótica. Por otro lado, el uso de los modelos geométricos que relacionan distintas vistas de una escena permite rechazar emparejamientos erróneos de características visuales entre imágenes, y de este modo robustecer el proceso de control mediante visión. Nuestro trabajo presenta dos técnicas de control visual para ser usadas por un robot moviéndose en el plano del suelo. En primer lugar, proponemos un nuevo método para homing visual, que emplea la información dada por un conjunto de imágenes de referencia adquiridas previamente en el entorno, y las imágenes que toma el robot a lo largo de su movimiento. Con el objeto de sacar partido de las cualidades de la visión omnidireccional, nuestro método de homing es puramente angular, y no emplea información alguna sobre distancia. Esta característica, unida al hecho de que el movimiento se realiza en un plano, motiva el empleo del modelo geométrico dado por el tensor trifocal 1D. En particular, las restricciones geométricas impuestas por dicho tensor, que puede ser calculado a partir de correspondencias de puntos entre tres imágenes, mejoran la robustez del control en presencia de errores de emparejamiento. El interés de nuestra propuesta reside en que el método de control empleado calcula las velocidades del robot a partir de información únicamente angular, siendo ésta muy precisa en las cámaras omnidireccionales. Además, presentamos un procedimiento que calcula las relaciones angulares entre las vistas disponibles de manera indirecta, sin necesidad de que haya información visual compartida entre todas ellas. La técnica descrita se puede clasificar como basada en imagen (image-based), dado que no precisa estimar la localización ni utiliza información 3D. El robot converge a la posición objetivo sin conocer la información métrica sobre la trayectoria seguida. Para algunas aplicaciones, como la evitación de obstáculos, puede ser necesario disponer de mayor información sobre el movimiento 3D realizado. Con esta idea en mente, presentamos un nuevo método de control visual basado en entradas sinusoidales. Las sinusoides son funciones con propiedades matemáticas bien conocidas y de variación suave, lo cual las hace adecuadas para su empleo en maniobras de aparcamiento de vehículos. A partir de las velocidades de variación sinusoidal que definimos en nuestro diseño, obtenemos las expresiones analíticas de la evolución de las variables de estado del robot. Además, basándonos en dichas expresiones, proponemos un método de control mediante realimentación del estado. La estimación del estado del robot se obtiene a partir del tensor trifocal 1D calculado entre la vista objetivo, la vista inicial y la vista actual del robot. Mediante este control sinusoidal, el robot queda alineado con la posición objetivo. En un segundo paso, efectuamos la corrección de la profundidad mediante una ley de control definida directamente en términos del tensor trifocal 1D. El funcionamiento de los dos controladores propuestos en el trabajo se ilustra mediante simulaciones, y con el objeto de respaldar su viabilidad se presentan análisis de estabilidad y resultados de simulaciones y de experimentos con imágenes reales

Migration from Teleoperation to Autonomy via Modular Sensor and Mobility Bricks

In this thesis, the teleoperated communications of a Remotec ANDROS robot have been reverse engineered. This research has used the information acquired through the reverse engineering process to enhance the teleoperation and add intelligence to the initially automated robot. The main contribution of this thesis is the implementation of the mobility brick paradigm, which enables autonomous operations, using the commercial teleoperated ANDROS platform. The brick paradigm is a generalized architecture for a modular approach to robotics. This architecture and the contribution of this thesis are a paradigm shift from the proprietary commercial models that exist today. The modular system of sensor bricks integrates the transformed mobility platform and defines it as a mobility brick. In the wall following application implemented in this work, the mobile robotic system acquires intelligence using the range sensor brick. This application illustrates a way to alleviate the burden on the human operator and delegate certain tasks to the robot. Wall following is one among several examples of giving a degree of autonomy to an essentially teleoperated robot through the Sensor Brick System. Indeed once the proprietary robot has been altered into a mobility brick; the possibilities for autonomy are numerous and vary with different sensor bricks. The autonomous system implemented is not a fixed-application robot but rather a non-specific autonomy capable platform. Meanwhile the native controller and the computer-interfaced teleoperation are still available when necessary. Rather than trading off by switching from teleoperation to autonomy, this system provides the flexibility to switch between the two at the operator’s command. The contributions of this thesis reside in the reverse engineering of the original robot, its upgrade to a computer-interfaced teleoperated system, the mobility brick paradigm and the addition of autonomy capabilities. The application of a robot autonomously following a wall is subsequently implemented, tested and analyzed in this work. The analysis provides the programmer with information on controlling the robot and launching the autonomous function. The results are conclusive and open up the possibilities for a variety of autonomous applications for mobility platforms using modular sensor bricks

Learning cognitive maps: Finding useful structure in an uncertain world

In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg