Genome variations: Effects on the robustness of neuroevolved control for swarm robotics systems

Manual design of self-organized behavioral control for swarms of robots is a complex task. Neuroevolution has proved a viable alternative given its capacity to automatically synthesize controllers. In this paper, we introduce the concept of Genome Variations (GV) in the neuroevolution of behavioral control for robotic swarms. In an evolutionary setup with GV, a slight mutation is applied to the evolving neural network parameters before they are copied to the robots in a swarm. The genome variation is individual to each robot, thereby generating a slightly heterogeneous swarm. GV represents a novel approach to the evolution of robust behaviors, expected to generate more stable and robust individual controllers, and bene t swarm behaviors that can deal with small heterogeneities in the behavior of other members in the swarm. We conduct experiments using an aggregation task, and compare the evolved solutions to solutions evolved under ideal, noise-free conditions, and to solutions evolved with traditional sensor noise.info:eu-repo/semantics/acceptedVersio

A cooperative active perception approach for swarm robotics

More than half a century after modern robotics first emerged, we still face a landscape in which most of the work done by robots is predetermined, rather than autonomous. A strong understanding of the environment is one of the key factors for autonomy, enabling the robots to make correct decisions based on the environment surrounding them. Classic methods for obtaining robotic controllers are based on manual specification, but become less trivial as the complexity scales. Artificial intelligence methods like evolutionary algorithms were introduced to synthesize robotic controllers by optimizing an artificial neural network to a given fitness function that measures the robots’ performance to solve a predetermined task. In this work, a novel approach to swarm robotics environment perception is studied, with a behavior model based on the cooperative identification of objects that fly around an environment, followed by an action based on the result of the identification process. Controllers are obtained via evolutionary methods. Results show a controller with a high identification and correct decision rates. The work is followed by a study on scaling up that approach to multiple environments. Experiments are done on terrain, marine and aerial environments, as well as on ideal, noisy and hybrid scenarios. In the hybrid scenario, different evolution samples are done in different environments. Results show the way these controllers are able to adapt to each scenario and conclude a hybrid evolution is the best fit to generate a more robust and environment independent controller to solve our task.Mais de um século após a robótica moderna ter surgido, ainda nos deparamos com um cenário onde a maioria do trabalho executado por robôs é pré-determinado, ao invés de autónomo. Uma forte compreensão do ambiente é um dos pontos chave para a autonomia, permitindo aos robôs tomarem decisões corretas baseadas no ambiente que os rodeia. Abordagens mais clássicas para obter controladores de robótica são baseadas na especificação manual, mas tornam-se menos apropriadas à medida que a complexidade aumenta. Métodos de inteligência artificial como algoritmos evolucionários foram introduzidos para obter controladores de robótica através da otimização de uma rede neuronal artificial para uma função de fitness que mede a aptidão dos robôs para resolver uma determinada tarefa. Neste trabalho, é apresentada uma nova abordagem para perceção do ambiente por um enxame de robôs, com um modelo de comportamento baseado na identificação cooperativa de objetos que circulam no ambiente, seguida de uma atuação baseada no resultado da identificação. Os controladores são obtidos através de métodos evolucionários. Os resultados apesentam um controlador com uma alta taxa de identificação e de decisão. Segue-se um estudo sobre o escalonamento da abordagem a múltiplos ambientes. São feitas experiencias num ambiente terrestre, marinho e aéreo, bem como num contexto ideal, ruidoso e híbrido. No contexto híbrido, diferentes samples da evolução ocorrem em diferentes ambientes. Os resultados demonstram a forma como cada controlador se adapta aos restantes ambientes e concluem que a evolução híbrida foi a mais capaz de gerar um controlador robusto e transversal aos diferentes ambientes. Palavras-chave: Robótica evolucionária, Sistemas multi-robô, Cooperação, Perceção, Identificação de objetos, Inteligência artificial, Aprendizagem automática, Redes neuronais, Múltiplos ambientes

Engineering evolutionary control for real-world robotic systems

Evolutionary Robotics (ER) is the field of study concerned with the application of evolutionary computation to the design of robotic systems. Two main issues have prevented ER from being applied to real-world tasks, namely scaling to complex tasks and the transfer of control to real-robot systems. Finding solutions to complex tasks is challenging for evolutionary approaches due to the bootstrap problem and deception. When the task goal is too difficult, the evolutionary process will drift in regions of the search space with equally low levels of performance and therefore fail to bootstrap. Furthermore, the search space tends to get rugged (deceptive) as task complexity increases, which can lead to premature convergence. Another prominent issue in ER is the reality gap. Behavioral control is typically evolved in simulation and then only transferred to the real robotic hardware when a good solution has been found. Since simulation is an abstraction of the real world, the accuracy of the robot model and its interactions with the environment is limited. As a result, control evolved in a simulator tends to display a lower performance in reality than in simulation. In this thesis, we present a hierarchical control synthesis approach that enables the use of ER techniques for complex tasks in real robotic hardware by mitigating the bootstrap problem, deception, and the reality gap. We recursively decompose a task into sub-tasks, and synthesize control for each sub-task. The individual behaviors are then composed hierarchically. The possibility of incrementally transferring control as the controller is composed allows transferability issues to be addressed locally in the controller hierarchy. Our approach features hybridity, allowing different control synthesis techniques to be combined. We demonstrate our approach in a series of tasks that go beyond the complexity of tasks where ER has been successfully applied. We further show that hierarchical control can be applied in single-robot systems and in multirobot systems. Given our long-term goal of enabling the application of ER techniques to real-world tasks, we systematically validate our approach in real robotic hardware. For one of the demonstrations in this thesis, we have designed and built a swarm robotic platform, and we show the first successful transfer of evolved and hierarchical control to a swarm of robots outside of controlled laboratory conditions.A Robótica Evolutiva (RE) é a área de investigação que estuda a aplicação de computação evolutiva na conceção de sistemas robóticos. Dois principais desafios têm impedido a aplicação da RE em tarefas do mundo real: a dificuldade em solucionar tarefas complexas e a transferência de controladores evoluídos para sistemas robóticos reais. Encontrar soluções para tarefas complexas é desafiante para as técnicas evolutivas devido ao bootstrap problem e à deception. Quando o objetivo é demasiado difícil, o processo evolutivo tende a permanecer em regiões do espaço de procura com níveis de desempenho igualmente baixos, e consequentemente não consegue inicializar. Por outro lado, o espaço de procura tende a enrugar à medida que a complexidade da tarefa aumenta, o que pode resultar numa convergência prematura. Outro desafio na RE é a reality gap. O controlo robótico é tipicamente evoluído em simulação, e só é transferido para o sistema robótico real quando uma boa solução tiver sido encontrada. Como a simulação é uma abstração da realidade, a precisão do modelo do robô e das suas interações com o ambiente é limitada, podendo resultar em controladores com um menor desempenho no mundo real. Nesta tese, apresentamos uma abordagem de síntese de controlo hierárquica que permite o uso de técnicas de RE em tarefas complexas com hardware robótico real, mitigando o bootstrap problem, a deception e a reality gap. Decompomos recursivamente uma tarefa em sub-tarefas, e sintetizamos controlo para cada subtarefa. Os comportamentos individuais são então compostos hierarquicamente. A possibilidade de transferir o controlo incrementalmente à medida que o controlador é composto permite que problemas de transferibilidade possam ser endereçados localmente na hierarquia do controlador. A nossa abordagem permite o uso de diferentes técnicas de síntese de controlo, resultando em controladores híbridos. Demonstramos a nossa abordagem em várias tarefas que vão para além da complexidade das tarefas onde a RE foi aplicada. Também mostramos que o controlo hierárquico pode ser aplicado em sistemas de um robô ou sistemas multirobô. Dado o nosso objetivo de longo prazo de permitir o uso de técnicas de RE em tarefas no mundo real, concebemos e desenvolvemos uma plataforma de robótica de enxame, e mostramos a primeira transferência de controlo evoluído e hierárquico para um exame de robôs fora de condições controladas de laboratório.This work has been supported by the Portuguese Foundation for Science and Technology (Fundação para a Ciência e Tecnologia) under the grants SFRH/BD/76438/2011, EXPL/EEI-AUT/0329/2013, and by Instituto de Telecomunicações under the grant UID/EEA/50008/2013

Neuroevolution trajectory networks : illuminating the evolution of artificial neural networks

Neuroevolution is the discipline whereby ANNs are automatically generated using EC. This field began with the evolution of dense (shallow) neural networks for reinforcement learning task; neurocontrollers capable of evolving specific behaviours as required. Since then, neuroevolution has been used to discover architectures and hyperparameters of Deep Neural Networks, in ways never before conceived by human experts, with many achieving state-of-the-art results. Similar to other types of EAs, there is a wide variety of neuroevolution algorithms constantly being introduced. However, there is a lack of effective tools to examine these systems and assess whether they share underlying principles. This thesis proposes Neuroevolution Trajectory Networks (NTNs), an advanced visualisation tool that leverages complex networks to explore the intrinsic mechanisms inherent in the evolution of neural networks. In this research the tool was developed as a specialised version of Search Trajectory Networks, and it was particularly instantiated to illuminate the behaviour of algorithms navigating neuroevolution search spaces. Throughout the progress, this technique has been progressively applied from systems of shallow network evolution, to deep neural networks. The examination has focused on explicit characteristics of neuroevolution system. Specifically, the learnings achieved highlighted the importance of understanding the role of recombination in neuroevolution, revealing critical inefficiencies that hinder overall algorithm performance. A relation between neurocontrollers' diversity and exploration exists, as topological structures can influence the behavioural characterisations and the diversity generation of different search strategies. Furthermore, our analytical tool has offered insights into the favoured dynamics of transfer learning paradigm in the deep neuroevolution of Convolutional Neural Networks; shedding light on promising avenues for further research and development. All of the above have offered substantial evidence that this advanced tool can be regarded as a specialised observational technique to better understand the inner mechanics of neuroevolution and its specific components, beyond the assessment of accuracy and performance alone. This is done so that collective efforts can be concentrated on aspects that can further enhance the evolution of neural networks. Illuminating their search spaces can be seen as a first step to analysing neural network compositions