Abstractions, Analysis Techniques, and Synthesis of Scalable Control Strategies for Robot Swarms

Tasks that require parallelism, redundancy, and adaptation to dynamic, possibly hazardous environments can potentially be performed very efficiently and robustly by a swarm robotic system. Such a system would consist of hundreds or thousands of anonymous, resource-constrained robots that operate autonomously, with little to no direct human supervision. The massive parallelism of a swarm would allow it to perform effectively in the event of robot failures, and the simplicity of individual robots facilitates a low unit cost. Key challenges in the development of swarm robotic systems include the accurate prediction of swarm behavior and the design of robot controllers that can be proven to produce a desired macroscopic outcome. The controllers should be scalable, meaning that they ensure system operation regardless of the swarm size. This thesis presents a comprehensive approach to modeling a swarm robotic system, analyzing its performance, and synthesizing scalable control policies that cause the populations of different swarm elements to evolve in a specified way that obeys time and efficiency constraints. The control policies are decentralized, computed a priori, implementable on robots with limited sensing and communication capabilities, and have theoretical guarantees on performance. To facilitate this framework of abstraction and top-down controller synthesis, the swarm is designed to emulate a system of chemically reacting molecules. The majority of this work considers well-mixed systems when there are interaction-dependent task transitions, with some modeling and analysis extensions to spatially inhomogeneous systems. The methodology is applied to the design of a swarm task allocation approach that does not rely on inter-robot communication, a reconfigurable manufacturing system, and a cooperative transport strategy for groups of robots. The third application incorporates observations from a novel experimental study of the mechanics of cooperative retrieval in Aphaenogaster cockerelli ants. The correctness of the abstractions and the correspondence of the evolution of the controlled system to the target behavior are validated with computer simulations. The investigated applications form the building blocks for a versatile swarm system with integrated capabilities that have performance guarantees

Swarm intelligence techniques for optimization and management tasks insensor networks

The main contributions of this thesis are located in the domain of wireless sensor netorks. More in detail, we introduce energyaware algorithms and protocols in the context of the following topics: self-synchronized duty-cycling in networks with energy harvesting capabilities, distributed graph coloring and minimum energy broadcasting with realistic antennas. In the following, we review the research conducted in each case. We propose a self-synchronized duty-cycling mechanism for sensor networks. This mechanism is based on the working and resting phases of natural ant colonies, which show self-synchronized activity phases. The main goal of duty-cycling methods is to save energy by efficiently alternating between different states. In the case at hand, we considered two different states: the sleep state, where communications are not possible and energy consumption is low; and the active state, where communication result in a higher energy consumption. In order to test the model, we conducted an extensive experimentation with synchronous simulations on mobile networks and static networks, and also considering asynchronous networks. Later, we extended this work by assuming a broader point of view and including a comprehensive study of the parameters. In addition, thanks to a collaboration with the Technical University of Braunschweig, we were able to test our algorithm in the real sensor network simulator Shawn (http://shawn.sf.net). The second part of this thesis is devoted to the desynchronization of wireless sensor nodes and its application to the distributed graph coloring problem. In particular, our research is inspired by the calling behavior of Japanese tree frogs, whose males use their calls to attract females. Interestingly, as female frogs are only able to correctly localize the male frogs when their calls are not too close in time, groups of males that are located nearby each other desynchronize their calls. Based on a model of this behavior from the literature, we propose a novel algorithm with applications to the field of sensor networks. More in detail, we analyzed the ability of the algorithm to desynchronize neighboring nodes. Furthermore, we considered extensions of the original model, hereby improving its desynchronization capabilities.To illustrate the potential benefits of desynchronized networks, we then focused on distributed graph coloring. Later, we analyzed the algorithm more extensively and show its performance on a larger set of benchmark instances. The classical minimum energy broadcast (MEB) problem in wireless ad hoc networks, which is well-studied in the scientific literature, considers an antenna model that allows the adjustment of the transmission power to any desired real value from zero up to the maximum transmission power level. However, when specifically considering sensor networks, a look at the currently available hardware shows that this antenna model is not very realistic. In this work we re-formulate the MEB problem for an antenna model that is realistic for sensor networks. In this antenna model transmission power levels are chosen from a finite set of possible ones. A further contribution concerns the adaptation of an ant colony optimization algorithm --currently being the state of the art for the classical MEB problem-- to the more realistic problem version, the so-called minimum energy broadcast problem with realistic antennas (MEBRA). The obtained results show that the advantage of ant colony optimization over classical heuristics even grows when the number of possible transmission power levels decreases. Finally we build a distributed version of the algorithm, which also compares quite favorably against centralized heuristics from the literature.Las principles contribuciones de esta tesis se encuentran en el domino de las redes de sensores inalámbricas. Más en detalle, introducimos algoritmos y protocolos que intentan minimizar el consumo energético para los siguientes problemas: gestión autosincronizada de encendido y apagado de sensores con capacidad para obtener energía del ambiente, coloreado de grafos distribuido y broadcasting de consumo mínimo en entornos con antenas reales. En primer lugar, proponemos un sistema capaz de autosincronizar los ciclos de encendido y apagado de los nodos de una red de sensores. El mecanismo está basado en las fases de trabajo y reposo de las colonias de hormigas tal y como estas pueden observarse en la naturaleza, es decir, con fases de actividad autosincronizadas. El principal objectivo de este tipo de técnicas es ahorrar energía gracias a alternar estados de forma eficiente. En este caso en concreto, consideramos dos estados diferentes: el estado dormido, en el que los nodos no pueden comunicarse y el consumo energético es bajo; y el estado activo, en el que las comunicaciones propician un consumo energético elevado. Con el objetivo de probar el modelo, se ha llevado a cabo una extensa experimentación que incluye tanto simulaciones síncronas en redes móviles y estáticas, como simulaciones en redes asíncronas. Además, este trabajo se extendió asumiendo un punto de vista más amplio e incluyendo un detallado estudio de los parámetros del algoritmo. Finalmente, gracias a la colaboración con la Technical University of Braunschweig, tuvimos la oportunidad de probar el mecanismo en el simulador realista de redes de sensores, Shawn (http://shawn.sf.net). La segunda parte de esta tesis está dedicada a la desincronización de nodos en redes de sensores y a su aplicación al problema del coloreado de grafos de forma distribuida. En particular, nuestra investigación está inspirada por el canto de las ranas de árbol japonesas, cuyos machos utilizan su canto para atraer a las hembras. Resulta interesante que debido a que las hembras solo son capaces de localizar las ranas macho cuando sus cantos no están demasiado cerca en el tiempo, los grupos de machos que se hallan en una misma región desincronizan sus cantos. Basado en un modelo de este comportamiento que se encuentra en la literatura, proponemos un nuevo algoritmo con aplicaciones al campo de las redes de sensores. Más en detalle, analizamos la habilidad del algoritmo para desincronizar nodos vecinos. Además, consideramos extensiones del modelo original, mejorando su capacidad de desincronización. Para ilustrar los potenciales beneficios de las redes desincronizadas, nos centramos en el problema del coloreado de grafos distribuido que tiene relación con diferentes tareas habituales en redes de sensores. El clásico problema del broadcasting de consumo mínimo en redes ad hoc ha sido bien estudiado en la literatura. El problema considera un modelo de antena que permite transmitir a cualquier potencia elegida (hasta un máximo establecido por el dispositivo). Sin embargo, cuando se trabaja de forma específica con redes de sensores, un vistazo al hardware actualmente disponible muestra que este modelo de antena no es demasiado realista. En este trabajo reformulamos el problema para el modelo de antena más habitual en redes de sensores. En este modelo, los niveles de potencia de transmisión se eligen de un conjunto finito de posibilidades. La siguiente contribución consiste en en la adaptación de un algoritmo de optimización por colonias de hormigas a la versión más realista del problema, también conocida como broadcasting de consumo mínimo con antenas realistas. Los resultados obtenidos muestran que la ventaja de este método sobre heurísticas clásicas incluso crece cuando el número de posibles potencias de transmisión decrece. Además, se ha presentado una versión distribuida del algoritmo, que también se compara de forma bastante favorable contra las heurísticas centralizadas conocidas

Enumeration, conformation sampling and population of libraries of peptide macrocycles for the search of chemotherapeutic cardioprotection agents

Peptides are uniquely endowed with features that allow them to perturb previously difficult to drug biomolecular targets. Peptide macrocycles in particular have seen a flurry of recent interest due to their enhanced bioavailability, tunability and specificity. Although these properties make them attractive hit-candidates in early stage drug discovery, knowing which peptides to pursue is non‐trivial due to the magnitude of the peptide sequence space. Computational screening approaches show promise in their ability to address the size of this search space but suffer from their inability to accurately interrogate the conformational landscape of peptide macrocycles. We developed an in‐silico compound enumerator that was tasked with populating a conformationally laden peptide virtual library. This library was then used in the search for cardio‐protective agents (that may be administered, reducing tissue damage during reperfusion after ischemia (heart attacks)). Our enumerator successfully generated a library of 15.2 billion compounds, requiring the use of compression algorithms, conformational sampling protocols and management of aggregated compute resources in the context of a local cluster. In the absence of experimental biophysical data, we performed biased sampling during alchemical molecular dynamics simulations in order to observe cyclophilin‐D perturbation by cyclosporine A and its mitochondrial targeted analogue. Reliable intermediate state averaging through a WHAM analysis of the biased dynamic pulling simulations confirmed that the cardio‐protective activity of cyclosporine A was due to its mitochondrial targeting. Paralleltempered solution molecular dynamics in combination with efficient clustering isolated the essential dynamics of a cyclic peptide scaffold. The rapid enumeration of skeletons from these essential dynamics gave rise to a conformation laden virtual library of all the 15.2 Billion unique cyclic peptides (given the limits on peptide sequence imposed). Analysis of this library showed the exact extent of physicochemical properties covered, relative to the bare scaffold precursor. Molecular docking of a subset of the virtual library against cyclophilin‐D showed significant improvements in affinity to the target (relative to cyclosporine A). The conformation laden virtual library, accessed by our methodology, provided derivatives that were able to make many interactions per peptide with the cyclophilin‐D target. Machine learning methods showed promise in the training of Support Vector Machines for synthetic feasibility prediction for this library. The synergy between enumeration and conformational sampling greatly improves the performance of this library during virtual screening, even when only a subset is used

Deep Neuroevolution: Smart City Applications

Particularmente, la contribución de esta tesis se centra en cuatro aspectos: Primero, proponemos la técnica Mean Absolute Error Random Sampling (MRS) para estimar el rendimiento de una RNN, la cual se basa en la distribución del error observado en un muestreo aleatorio. Nuestros resultados muestran que MRS es una estimación fiable y de bajo coste computacional para predecir el rendimiento de una RNN. Segundo, diseñamos un algoritmo evolutivo (RESN) que explota MRS para optimizar la arquitectura de una RNN. RESN muestra resultados competitivos a la vez que reduce significativamente el tiempo. Tercero, en el contexto de la aplicación, proponemos soluciones para problemas de movilidad, electricidad y gestión de residuos inteligente, y hemos revisado el estado del arte de la ciudad inteligente y su relación con la informática. Cuarto, hemos desarrollado la biblioteca de software Deep Learning OPTimization (DLOPT), la cual está disponible bajo la licencia GNU GPL v3. Ésta contiene la mayor parte del trabajo realizado en esta tesis.El interés por desarrollar redes neuronales artificiales ha resurgido de la mano del Aprendizaje Profundo. En términos simples, el aprendizaje profundo consiste en diseñar y entrenar una red neuronal de gran complejidad y tamaño con una inmensa cantidad de datos. Esta creciente complejidad propone nuevos desafíos, siendo de especial relevancia la optimización del diseño dado un problema. Tradicionalmente, este problema ha sido resuelto en una combinación de conocimiento experto (humano) con prueba y error. Sin embargo, conforme la complejidad aumenta, este acercamiento se vuelve ineficiente (e impracticable). Esta tesis doctoral aborda el diseño de redes neuronales recurrentes (RNN), un tipo de red neuronal profunda, desde la neuroevolución. Concretamente, se combinan técnicas de aprendizaje automático con metaheurísticas avanzadas, con el fin de proveer una solución eficaz y eficiente. Por otra parte, se aplican las técnicas desarrolladas a problemas de la ciudad inteligente

ENVIRONMENTAL EFFECTS ON BEHAVIOR AND PHYSIOLOGY IN CRAYFISH

Despite dramatic morphological differences between animals from different taxa, several important features in organization and sensory system processing are similar across animals. Because of this similarity, a number of different organisms including mammals, insects, and decapod crustaceans serve as valuable model systems for understanding general principles of environmental effects. This research examines intrinsic and extrinsic factors by behaviorally and physiologically means to identify the impact of environmental conditions on two distinct crayfish species- Procambarus clarkii (surface) and Orconectes australis packardi (cave). The research identified behavioral and physiological responses in these two morphological and genetically distinct species. The studies also examined multiple levels of complexity including social behavior, an autonomic response, chemosensory capabilities and neuronal communication, identified comparative similarities/differences, addressed learning and environmental influences on learning and examined behavioral and cellular responses to high levels of carbon dioxide. I found environmental factors directly influence crayfish behavior of social interactions. Interactions were more aggressive, more intense and more likely to end with a physical confrontation when they took place \u27in water\u27 than \u27out of water\u27. The modified social interaction resulted in a altered fighting strategy. A study on motor task learning was undertaken which showed similar learning trends among these crayfish species despite their reliance on different sensory modalities. I also demonstrated learning was dependent on perceived stress by the organism. Previously trained crayfish inhibited from completing a task showed significant increase in an autonomic stress response. Studies on the behavioral and physiological responses to CO2 revealed that high [CO2] is a repellent in a concentration dependent manner. The autonomic responses in heart rate and an escape tailflip reflex shows complete cessation with high [CO2]. A mechanistic effect of CO2 is by blocking glutamate receptors at the neuromuscular junction and through inhibition of the motor nerve within the CNS

Moving Birds in Hawai'i: Assisted Colonisation in a Colonised Land

In September 2011, a delicate cargo of 24 Nihoa Millerbirds was carefully loaded by conservationists onto a ship for a three-day voyage to Laysan Island in the remote Northwest Hawaiian Islands. The goal of this effort was to establish a second population of this endangered species, an “insurance population” in the face of the mounting pressures of climate change and potential new biotic arrivals. But the millerbird, or ulūlu in Hawaiian, is just one of the many avian species to become the subject of this kind of “assisted colonisation.” In Hawai'i, and around the world, recent years have seen a broad range of efforts to safeguard species by finding them homes in new places. Thinking through the ulūlu project, this article explores the challenges and possibilities of assisted colonisation in this colonised land. What does it mean to move birds in the context of the long, and ongoing, history of dispossession of the Kānaka Maoli, the Native Hawaiian people? How are distinct but entangled process of colonisation, of unworlding, at work in the lives of both people and birds? Ultimately, this article explores how these diverse colonisations might be understood and told responsibly in an era of escalating loss and extinction