An experimental study on evolutionary reactive behaviors for mobile robots navigation

Mobile robot's navigation and obstacle avoidance in an unknown and static environment is analyzed in this paper. From the guidance of position sensors, artificial neural network (ANN) based controllers settle the desired trajectory between current and a target point. Evolutionary algorithms were used to choose the best controller. This approach, known as Evolutionary Robotics (ER), commonly resorts to very simple ANN architectures. Although they include temporal processing, most of them do not consider the learned experience in the controller's evolution. Thus, the ER research presented in this article, focuses on the specification and testing of the ANN based controllers implemented when genetic mutations are performed from one generation to another. Discrete-Time Recurrent Neural Networks based controllers were tested, with two variants: plastic neural networks (PNN) and standard feedforward (FFNN) networks. Also the way in which evolution was performed was also analyzed. As a result, controlled mutation do not exhibit major advantages against over the non controlled one, showing that diversity is more powerful than controlled adaptation.Facultad de Informátic

An experimental study on evolutionary reactive behaviors for mobile robots navigation

Mobile robot's navigation and obstacle avoidance in an unknown and static environment is analyzed in this paper. From the guidance of position sensors, artificial neural network (ANN) based controllers settle the desired trajectory between current and a target point. Evolutionary algorithms were used to choose the best controller. This approach, known as Evolutionary Robotics (ER), commonly resorts to very simple ANN architectures. Although they include temporal processing, most of them do not consider the learned experience in the controller's evolution. Thus, the ER research presented in this article, focuses on the specification and testing of the ANN based controllers implemented when genetic mutations are performed from one generation to another. Discrete-Time Recurrent Neural Networks based controllers were tested, with two variants: plastic neural networks (PNN) and standard feedforward (FFNN) networks. Also the way in which evolution was performed was also analyzed. As a result, controlled mutation do not exhibit major advantages against over the non controlled one, showing that diversity is more powerful than controlled adaptation.Facultad de Informátic

Neuro-Controllers, scalability and adaptation

A Layered Evolution (LE) paradigm based method for the generation of a neuron-controller is developed and verified through simulations and experimentally. It is intended to solve scalability issues in systems with many behavioral modules. Each and every module is a genetically evolved neuro-controller specialized in performing a different task. The main goal is to reach a combination of different basic behavioral elements using different artificial neural-network paradigms concerning mobile robot navigation in an unknown environment. The obtained controller is evaluated over different scenarios in a structured environment, ranging from a detailed simulation model to a real experiment. Finally most important implies are shown through several focuses

Sistema de percepción para un vehículo autónomo submarino

La necesidad de la industria off-shore para compartir información y recursos de energía a través de cables y tuberías submarinas conduce a un creciente despliegue de infraestructuras sumergidas. Esto requiere de un posterior mantenimiento preventivo. Los vehículos autónomos submarinos (AUVs) representan una alternativa para llevar a cabo esta tarea. En base a la percepción, estos vehículos deben estar equipados con distintos dispositivos de sensores como cámaras de vídeo, dispositivo rastreador electromagnético, sonar de barrido lateral, ecosonda muti-haz, como así también dispositivos de ubicación como sistema de posicionamiento global, sistema de navegación inercial, brújula, entre otros. Cada uno de estos dispositivos hay que tratarlos por separado para la captura e interpretación de datos, pero en conjunto para la búsqueda de conocimiento útil que modifique el comportamiento on-line de un AUV. Este documento presenta el diseño y desarrollo de una arquitectura de software de un sistema de percepción para un AUV (PS-AUV). La arquitectura emplea como entrada datos provenientes de dispositivos de sensores interconectados aplicando distintos procesos, y como salida, conocimiento que alimentará al modelo del mundo del robot que se encuentra implementado en forma de un sistema basado en conocimiento.Sociedad Argentina de Informática e Investigación Operativ

Neuro-Controllers, scalability and adaptation

A Layered Evolution (LE) paradigm based method for the generation of a neuron-controller is developed and verified through simulations and experimentally. It is intended to solve scalability issues in systems with many behavioral modules. Each and every module is a genetically evolved neuro-controller specialized in performing a different task. The main goal is to reach a combination of different basic behavioral elements using different artificial neural-network paradigms concerning mobile robot navigation in an unknown environment. The obtained controller is evaluated over different scenarios in a structured environment, ranging from a detailed simulation model to a real experiment. Finally most important implies are shown through several focuses.Red de Universidades con Carreras en Informática (RedUNCI

Estudio experimental sobre comportamientos reactivos - evolutivos en navegación de robots móviles

En este trabajo se analiza la navegación y la evasión de obstáculos para robots móviles en un ambiente no conocido, estático y simulado. A partir de la lectura de los sensores de proximidad, los controladores basados en Redes Neuronales Artificiales (RNA) establecen la trayectoria deseada entre la posición actual y la posición objetivo. Algoritmos Evolutivos son usados en la selección del mejor controlador. Esta metodología de trabajo, es conocida como Robótica Evolutiva (RE), comúnmente utilizando simples arquitecturas de redes neuronales. A pesar de que los controladores desarrollados dentro de RE generalmente presentan procesamiento temporal, la mayoría no considera la experiencia obtenida en el proceso evolutivo del controlador. Por lo tanto, el presente trabajo, se refiere a la especificación y testeo de controladores neuronales, realizando mutaciones genéticas entre generaciones de controladores en base a la experiencia adquirida. Controladores basados en Redes Neuronales de Tiempo Discreto (TRNN) fueron desarrollados con dos variantes: Redes Neuronales Plásticas (PNN) y redes del tipo Feed-Forward (FFNN). Este trabajo demuestra que la mutación controlada no presenta mayores ventajas respecto de la no controlada, mostrando que la diversidad es más poderosa que la adaptación controlada.Eje: VI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Estudio experimental sobre comportamientos reactivos - evolutivos en navegación de robots móviles

En este trabajo se analiza la navegación y la evasión de obstáculos para robots móviles en un ambiente no conocido, estático y simulado. A partir de la lectura de los sensores de proximidad, los controladores basados en Redes Neuronales Artificiales (RNA) establecen la trayectoria deseada entre la posición actual y la posición objetivo. Algoritmos Evolutivos son usados en la selección del mejor controlador. Esta metodología de trabajo, es conocida como Robótica Evolutiva (RE), comúnmente utilizando simples arquitecturas de redes neuronales. A pesar de que los controladores desarrollados dentro de RE generalmente presentan procesamiento temporal, la mayoría no considera la experiencia obtenida en el proceso evolutivo del controlador. Por lo tanto, el presente trabajo, se refiere a la especificación y testeo de controladores neuronales, realizando mutaciones genéticas entre generaciones de controladores en base a la experiencia adquirida. Controladores basados en Redes Neuronales de Tiempo Discreto (TRNN) fueron desarrollados con dos variantes: Redes Neuronales Plásticas (PNN) y redes del tipo Feed-Forward (FFNN). Este trabajo demuestra que la mutación controlada no presenta mayores ventajas respecto de la no controlada, mostrando que la diversidad es más poderosa que la adaptación controlada.Eje: VI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Designing a Green Belt for Xalapa City Veracruz under current Mexican policies

Green Belts are oĞ en proposed as an alternative for containing urban sprawl, restoring ecological processes, recovering connectivity, and maintaining the multi-functionality that cities need. This article analyzes a proposed Green Belt for Xalapa, Veracruz, Mexico. It is spatially examined through GIS analysis and designed on the notion of Garden City as a strip to circumvent the city. Existing conditions are also discussed. Two existing conservation initiatives are compared to the proposed Green Belt strategy. Its establishment requires agreements between Xalapa and surrounding municipalities. The proposed strategy brings local government and citizens together to preserve the remaining vegetation and thus promote the well-being of local inhabitants

Large-area biomolecule nanopatterns on diblock copolymer surfaces for cell adhesion studies

Cell membrane receptors bind to extracellular ligands, triggering intracellular signal transduction pathways that result in specific cell function. Some receptors require to be associated forming clusters for effective signaling. Increasing evidences suggest that receptor clustering is subjected to spatially controlled ligand distribution at the nanoscale. Herein we present a method to produce in an easy, straightforward process, nanopatterns of biomolecular ligands to study ligand–receptor processes involving multivalent interactions. We based our platform in self-assembled diblock copolymers composed of poly(styrene) (PS) and poly(methyl methacrylate) (PMMA) that form PMMA nanodomains in a closed-packed hexagonal arrangement. Upon PMMA selective functionalization, biomolecular nanopatterns over large areas are produced. Nanopattern size and spacing can be controlled by the composition of the block-copolymer selected. Nanopatterns of cell adhesive peptides of different size and spacing were produced, and their impact in integrin receptor clustering and the formation of cell focal adhesions was studied. Cells on ligand nanopatterns showed an increased number of focal contacts, which were, in turn, more matured than those found in cells cultured on randomly presenting ligands. These findings suggest that our methodology is a suitable, versatile tool to study and control receptor clustering signaling and downstream cell behavior through a surface-based ligand patterning technique

Convergent Approaches for the Synthesis of the Anti-tumoral Peptide, Kahalalide F. Study of Orthogonal Protecting Groups

Kahalalide compounds are peptides that are isolated from a Hawaiian herbivorous marine species of mollusc, Elysia rufescens, and its diet, the green alga Bryopsis sp. Kahalalide F and its synthetic analogues are the most promising compounds of the Kahalalide family because they show anti-tumoral activity. Linear solid-phase syntheses of Kahalalide F have been reported. Here we describe several new improved synthetic routes based on convergent approaches with distinct orthogonal protection schemes for the preparation of Kahaladide analogues. These strategies allow a better control and characterization of the intermediates because more reactions are performed in solution. Five derivatives of Kahalalide F were synthesized using several convergent approaches