A Biomimetical Dynamic Window Approach to Navigation for Collaborative Control.

—Shared control is a strategy used in assistive plat forms to combine human and robot orders to achieve a goal. Col laborative control is a specific shared control approach, in which user’s and robot’s commands are merged into an emergent one in a continuous way. Robot commands tend to improve efficiency and safety. However, sometimes, assistance can be rejected by users when their commands are too altered. This provokes frustration and stress and, usually, decreases emergent efficiency. To improve acceptance, robot navigation algorithms can be adapted to mimic human behavior when possible. We propose a novel variation of the well-known dynamic window approach (DWA) that we call biomimetical DWA (BDWA). The BDWA relies on a reward func tion extracted from real traces from volunteers presenting different motor disabilities navigating in a hospital environment using a rol lator for support. We have compared the BDWA with other reactive algorithms in terms of similarity to paths completed by people with disabilities using a robotic rollator in a rehabilitation hospital unit. The BDWA outperforms all tested algorithms in terms of likeness to human paths and success rate.This work was supported in part by the Spanish Ministerio de Educacion y Ciencia through Project TEC2011-29106 and Project TEC2014-56256-C2-1-P, in part by the Hospital Regional Universitario of Malaga, and in part by the ´ Fondazione Santa Lucia of Rome. This paper was recommended by Associate Editor J. Wachs. (Corresponding author: Joaquin Ballesteros.

Advances in Robot Navigation

Robot navigation includes different interrelated activities such as perception - obtaining and interpreting sensory information; exploration - the strategy that guides the robot to select the next direction to go; mapping - the construction of a spatial representation by using the sensory information perceived; localization - the strategy to estimate the robot position within the spatial map; path planning - the strategy to find a path towards a goal location being optimal or not; and path execution, where motor actions are determined and adapted to environmental changes. This book integrates results from the research work of authors all over the world, addressing the abovementioned activities and analyzing the critical implications of dealing with dynamic environments. Different solutions providing adaptive navigation are taken from nature inspiration, and diverse applications are described in the context of an important field of study: social robotics

Monitoring and Control Framework for Advanced Power Plant Systems Using Artificial Intelligence Techniques

This dissertation presents the design, development, and simulation testing of a monitoring and control framework for dynamic systems using artificial intelligence techniques. A comprehensive monitoring and control system capable of detecting, identifying, evaluating, and accommodating various subsystem failures and upset conditions is presented. The system is developed by synergistically merging concepts inspired from the biological immune system with evolutionary optimization algorithms and adaptive control techniques.;The proposed methodology provides the tools for addressing the complexity and multi-dimensionality of the modern power plants in a comprehensive and integrated manner that classical approaches cannot achieve. Current approaches typically address abnormal condition (AC) detection of isolated subsystems of low complexity, affected by specific AC involving few features with limited identification capability. They do not attempt AC evaluation and mostly rely on control system robustness for accommodation. Addressing the problem of power plant monitoring and control under AC at this level of completeness has not yet been attempted.;Within the proposed framework, a novel algorithm, namely the partition of the universe, was developed for building the artificial immune system self. As compared to the clustering approach, the proposed approach is less computationally intensive and facilitates the use of full-dimensional self for system AC detection, identification, and evaluation. The approach is implemented in conjunction with a modified and improved dendritic cell algorithm. It allows for identifying the failed subsystems without previous training and is extended to address the AC evaluation using a novel approach.;The adaptive control laws are designed to augment the performance and robustness of baseline control laws under normal and abnormal operating conditions. Artificial neural network-based and artificial immune system-based approaches are developed and investigated for an advanced power plant through numerical simulation.;This dissertation also presents the development of an interactive computational environment for the optimization of power plant control system using evolutionary techniques with immunity-inspired enhancements. Several algorithms mimicking mechanisms of the immune system of superior organisms, such as cloning, affinity-based selection, seeding, and vaccination are used. These algorithms are expected to enhance the computational effectiveness, improve convergence, and be more efficient in handling multiple local extrema, through an adequate balance between exploration and exploitation.;The monitoring and control framework formulated in this dissertation applies to a wide range of technical problems. The proposed methodology is demonstrated with promising results using a high validity DynsimRTM model of the acid gas removal unit that is part of the integrated gasification combined cycle power plant available at West Virginia University AVESTAR Center. The obtained results show that the proposed system is an efficient and valuable technique to be applied to a real world application. The implementation of this methodology can potentially have significant impacts on the operational safety of many complex systems

Hybrid approaches for mobile robot navigation

The work described in this thesis contributes to the efficient solution of mobile robot navigation problems. A series of new evolutionary approaches is presented. Two novel evolutionary planners have been developed that reduce the computational overhead in generating plans of mobile robot movements. In comparison with the best-performing evolutionary scheme reported in the literature, the first of the planners significantly reduces the plan calculation time in static environments. The second planner was able to generate avoidance strategies in response to unexpected events arising from the presence of moving obstacles. To overcome limitations in responsiveness and the unrealistic assumptions regarding a priori knowledge that are inherent in planner-based and a vigation systems, subsequent work concentrated on hybrid approaches. These included a reactive component to identify rapidly and autonomously environmental features that were represented by a small number of critical waypoints. Not only is memory usage dramatically reduced by such a simplified representation, but also the calculation time to determine new plans is significantly reduced. Further significant enhancements of this work were firstly, dynamic avoidance to limit the likelihood of potential collisions with moving obstacles and secondly, exploration to identify statistically the dynamic characteristics of the environment. Finally, by retaining more extensive environmental knowledge gained during previous navigation activities, the capability of the hybrid navigation system was enhanced to allow planning to be performed for any start point and goal point

ARQUITECTURA DE CONTROL CONDUCTUAL PARA AGENTES INTELIGENTES (ARCHITECTURE OF BEHAVIORAL CONTROL FOR INTELLIGENT AGENTS)

En este trabajo se simula, por medio del lenguaje de programación NetLogo, el comportamiento adaptativo de un agente inteligente ante su medio ambiente. El comportamiento está regido por una arquitectura de control conductual de inspiración biológica que se implementa a partir de máquinas de estado. Con este tipo de arquitectura, se aborda la problemática de que el agente elija la respuesta conductual más apropiada en función de las circunstancias de su entorno y de la estimulación recibida. Se reporta y compara el funcionamiento del agente a partir de dos experimentos que utilizan 5 escenarios y 4 controladores. Las simulaciones de este comportamiento inteligente se pueden implementar en robots móviles autónomos, en agentes asistentes o tutores, o en aquellos agentes que buscan y recuperan información en bases de datos o en Internet (softbots).This work simulates, through the NetLogo programming language, the adaptive behavior of an intelligent agent in its environment. The behavior is governed by a behavioral control architecture of biological inspiration that is implemented from state machines. With this type of architecture, the problem addressed is that the agent chooses the most appropriate behavioral response depending on the circumstances of its environment and the stimulation received. The performance of the agent is reported and compared from two experiments using 5 scenarios and 4 controllers. The simulations of this intelligent behavior can be implemented in autonomous mobile robots, assistant agents or tutors, or in those agents that search and retrieve information in databases or the Internet (softbots)

Arquitectura de Comportamientos Reactivos para Agentes Robóticos basada en CBR

En los últimos tiempos se ha demostrado la importancia del aprendizaje en la Inteligencia humana, tanto en su vertiente de aprendizaje por observación como a través de la experiencia, como medio de identificar situaciones y predecir acciones o respuestas a partir de la información adquirida. Dado este esquema general de la Inteligencia Humana, parece razonable imitar su estructura y características en un intento por diseñar una arquitectura general de inteligencia aplicada a la Robótica. En este trabajo, inspirados por las teorías de Hawkins en su obra On Intelligence, hemos propuesto una arquitectura jerárquica de inteligencia en el que los diversos módulos se implementan a partir de Razonamiento basado en Casos ¿Case Based Reasoning (CBR)¿, una herramienta de IA especialmente apta para la adquisición de conocimiento a través del aprendizaje y para la predicción basada en similitud de información. Dentro de esta arquitectura la presente tesis se centra en las capas inferiores, las de tipo reactivo, expresadas en forma de comportamientos básicos, que implementan conductas sencillas pero indispensables para el funcionamiento de un robot. Estos comportamientos han sido tradicionalmente diseñados de forma algorítmica, con la dificultad que esto entraña en muchos casos por el desconocimiento de sus aspectos intrínsecos. Además, carecen de la capacidad de adaptarse ante nuevas situaciones no previstas y adquirir nuevos conocimientos a través del funcionamiento del robot, algo indispensable si se pretende que éste se desenvuelva en ambientes dinámicos y no estructurados. El trabajo de esta tesis considera la implementación de comportamientos reactivos con capacidad de aprendizaje, como forma de superar los inconvenientes anteriormente mencionados consiguiendo al mismo tiempo una mejor integración en la arquitectura general de Inteligencia considerada, en la cual el aprendizaje ocupa el papel principal. Así, se proponen y analizan diversas alternativas de diseño de comportamientos reactivos, construidos a través de sistemas CBR con capacidad de aprendizaje. En particular se estudia i) la problemática de selección, organización, y representación de la información como recipiente del conocimiento de los comportamientos;ii) los problemas asociados a la escalabilidad de esta información; iii) los aspectos que acompañan al proceso de predicción mediante la recuperación de la respuesta de experiencias previas similares a la presentada; iv) la identificación de la respuesta no solo con la acción a tomar por parte del comportamiento sino con un concepto que represente la situación presentada; y v) la adaptación y evaluación de la respuesta para incorporar nuevas situaciones como nuevo conocimiento del sistema. También se analiza la organización de comportamientos básicos que permite obtener, a través de sus interacciones, comportamientos emergentes de nivel superior aún dentro de un alcance reactivo. Todo ello se prueba con un robot real y con un simulador, en una variante de un escenario de aplicación clásico en Robótica, como es la competición Robocup. La elaboración de esta tesis ha supuesto, además de los aspectos puramente investigadores, un esfuerzo adicional en el desarrollo de las herramientas y metodología de pruebas necesarias para su realización. En este sentido, se ha programado un primer prototipo de marco de implementación de comportamientos reactivos con aprendizaje, basados en CBR, para la plataforma de desarrollo robótico Tekkotsu

An Empirical Evaluation of Communication and Coordination Effectiveness in Autonomous Reactive Multiagent Systems

This thesis describes experiments designed to measure the effect of collaborative communication on task performance of a multiagent system. A discrete event simulation was developed to model a multi-agent system completing a task to find and collect food resources, with the ability to substitute various communication and coordination methods. Experiments were conducted to find the effects of the various communication methods on completion of the task to find and harvest the food resources. Results show that communication decreases the time required to complete the task. However, all communication methods do not fare equally well. In particular, results indicate that the communication model of the bee is a particularly effective method of agent communication and collaboration. Furthermore, results indicate that direct communication with additional information content provides better completion results. Cost-benefit models show some conflicting information, indicating that the increased performance may not offset the additional cost of achieving that performance

TRIZ Future Conference 2004

TRIZ the Theory of Inventive Problem Solving is a living science and a practical methodology: millions of patents have been examined to look for principles of innovation and patterns of excellence. Large and small companies are using TRIZ to solve problems and to develop strategies for future technologies. The TRIZ Future Conference is the annual meeting of the European TRIZ Association, with contributions from everywhere in the world. The aims of the 2004 edition are the integration of TRIZ with other methodologies and the dissemination of systematic innovation practices even through SMEs: a broad spectrum of subjects in several fields debated with experts, practitioners and TRIZ newcomers

Neuromorphic auditory computing: towards a digital, event-based implementation of the hearing sense for robotics

In this work, it is intended to advance on the development of the neuromorphic audio processing systems in robots through the implementation of an open-source neuromorphic cochlea, event-based models of primary auditory nuclei, and their potential use for real-time robotics applications. First, the main gaps when working with neuromorphic cochleae were identified. Among them, the accessibility and usability of such sensors can be considered as a critical aspect. Silicon cochleae could not be as flexible as desired for some applications. However, FPGA-based sensors can be considered as an alternative for fast prototyping and proof-of-concept applications. Therefore, a software tool was implemented for generating open-source, user-configurable Neuromorphic Auditory Sensor models that can be deployed in any FPGA, removing the aforementioned barriers for the neuromorphic research community. Next, the biological principles of the animals' auditory system were studied with the aim of continuing the development of the Neuromorphic Auditory Sensor. More specifically, the principles of binaural hearing were deeply studied for implementing event-based models to perform real-time sound source localization tasks. Two different approaches were followed to extract inter-aural time differences from event-based auditory signals. On the one hand, a digital, event-based design of the Jeffress model was implemented. On the other hand, a novel digital implementation of the Time Difference Encoder model was designed and implemented on FPGA. Finally, three different robotic platforms were used for evaluating the performance of the proposed real-time neuromorphic audio processing architectures. An audio-guided central pattern generator was used to control a hexapod robot in real-time using spiking neural networks on SpiNNaker. Then, a sensory integration application was implemented combining sound source localization and obstacle avoidance for autonomous robots navigation. Lastly, the Neuromorphic Auditory Sensor was integrated within the iCub robotic platform, being the first time that an event-based cochlea is used in a humanoid robot. Then, the conclusions obtained are presented and new features and improvements are proposed for future works.En este trabajo se pretende avanzar en el desarrollo de los sistemas de procesamiento de audio neuromórficos en robots a través de la implementación de una cóclea neuromórfica de código abierto, modelos basados en eventos de los núcleos auditivos primarios, y su potencial uso para aplicaciones de robótica en tiempo real. En primer lugar, se identificaron los principales problemas a la hora de trabajar con cócleas neuromórficas. Entre ellos, la accesibilidad y usabilidad de dichos sensores puede considerarse un aspecto crítico. Los circuitos integrados analógicos que implementan modelos cocleares pueden no pueden ser tan flexibles como se desea para algunas aplicaciones específicas. Sin embargo, los sensores basados en FPGA pueden considerarse una alternativa para el desarrollo rápido y flexible de prototipos y aplicaciones de prueba de concepto. Por lo tanto, en este trabajo se implementó una herramienta de software para generar modelos de sensores auditivos neuromórficos de código abierto y configurables por el usuario, que pueden desplegarse en cualquier FPGA, eliminando las barreras mencionadas para la comunidad de investigación neuromórfica. A continuación, se estudiaron los principios biológicos del sistema auditivo de los animales con el objetivo de continuar con el desarrollo del Sensor Auditivo Neuromórfico (NAS). Más concretamente, se estudiaron en profundidad los principios de la audición binaural con el fin de implementar modelos basados en eventos para realizar tareas de localización de fuentes sonoras en tiempo real. Se siguieron dos enfoques diferentes para extraer las diferencias temporales interaurales de las señales auditivas basadas en eventos. Por un lado, se implementó un diseño digital basado en eventos del modelo Jeffress. Por otro lado, se diseñó una novedosa implementación digital del modelo de codificador de diferencias temporales y se implementó en FPGA. Por último, se utilizaron tres plataformas robóticas diferentes para evaluar el rendimiento de las arquitecturas de procesamiento de audio neuromórfico en tiempo real propuestas. Se utilizó un generador central de patrones guiado por audio para controlar un robot hexápodo en tiempo real utilizando redes neuronales pulsantes en SpiNNaker. A continuación, se implementó una aplicación de integración sensorial que combina la localización de fuentes de sonido y la evitación de obstáculos para la navegación de robots autónomos. Por último, se integró el Sensor Auditivo Neuromórfico dentro de la plataforma robótica iCub, siendo la primera vez que se utiliza una cóclea basada en eventos en un robot humanoide. Por último, en este trabajo se presentan las conclusiones obtenidas y se proponen nuevas funcionalidades y mejoras para futuros trabajos

Growth Factor Gradient Formation and Release from PEG Microspheres for Nerve Regeneration.

Many biological processes depend on concentration gradients in signaling molecules. Thus, introduction of spatial patterning of proteins, while retaining activity and releasability, will be critical for the field of regenerative medicine. In particular, the area of nerve regeneration is in need of innovations to improve outcomes. Only about 25% of surgical patients with peripheral nerve damage (~200,000 surgical interventions performed each year) regain full motor function with less than 3% regaining sensation. The use of nerve guidance conduits (NGC’s) which are filled with biomimetic scaffolds is one treatment being explored. These scaffolds, however, lack the spatial patterning of proteins found in native tissue. Glial-cell line derived neurotrophic factor (GDNF), a potent stimulator of axon regeneration, is one such protein that, if contained within the scaffold and conformed to a particular concentration profile, could greatly enhance neural regeneration. The object of this work is to utilize poly(ethylene glycol) (PEG) microspheres to accomplish this spatial patterning of GDNF and apply it to NGC’s. First, an approach utilizing the controllability of the PEG microsphere’s density (buoyancy) was explored. By creating the microspheres under varying conditions, incubation time and temperature, the cross-linking and, thus, the swelling rate of the microspheres could be controlled. This created microspheres of different densities that, upon centrifugation, would orient themselves within a scaffold, creating a gradient in the different microsphere types. GDNF loaded into a batch of microspheres would thusly be oriented within the scaffold along with that particular microsphere batch. Through this, gradients in GDNF were created. Heparin was also added to the microspheres to allow for reversible binding of GDNF. Next, gradients in reversibly bound GDNF were formed through sequential centrifugation of microsphere batches. For instance, a layer of GDNF loaded microspheres were formed into a scaffold followed by a layer of microspheres without GDNF on top of them. This created an initial step gradient in GDNF that, given time to release, would form a linear concentration gradient. Gradients formed by this method were visualized by fluorescent confocal microscopy and compared to Fickian models. Some conditions yielded profiles more linear than the model predictions, which persisted for over a week. Lastly, the sequential gradient formation was modified and applied to NGC’s. Before the scaffolds were ready for in vivo implantation, functionalities such as cell initiated degradability, cell adhesion, and inter-microsphere cross-linking were added. A plasmin degradable peptide sequence (GCGGVRNGGK) was incorporated into the microspheres. CLICK agents, laminin, and heparin (via a new binding chemistry) were attached to the microspheres to add inter-microsphere cross-linking, add cell adhesion, and heparin binding functionalities, respectively. GDNF gradient formation and activity retention were confirmed with these fully functionalized microspheres. Microsphere scaffolds with linear gradients in GDNF were then formed in silicone tubes which were transplanted into rats with severed sciatic nerves