Embodied Evolution in Collective Robotics: A Review

This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl

Synthesis of formation control for an aquatic swarm robotics system

Formations are the spatial organization of objects or entities according to some predefined pattern. They can be found in nature, in social animals such as fish schools, and insect colonies, where the spontaneous organization into emergent structures takes place. Formations have a multitude of applications such as in military and law enforcement scenarios, where they are used to increase operational performance. The concept is even present in collective sports modalities such as football, which use formations as a strategy to increase teams efficiency. Swarm robotics is an approach for the study of multi-robot systems composed of a large number of simple units, inspired in self-organization in animal societies. These have the potential to conduct tasks too demanding for a single robot operating alone. When applied to the coordination of such type of systems, formations allow for a coordinated motion and enable SRS to increase their sensing efficiency as a whole. In this dissertation, we present a virtual structure formation control synthesis for a multi-robot system. Control is synthesized through the use of evolutionary robotics, from where the desired collective behavior emerges, while displaying key-features such as fault tolerance and robustness. Initial experiments on formation control synthesis were conducted in simulation environment. We later developed an inexpensive aquatic robotic platform in order to conduct experiments in real world conditions. Our results demonstrated that it is possible to synthesize formation control for a multi-robot system making use of evolutionary robotics. The developed robotic platform was used in several scientific studies.As formações consistem na organização de objetos ou entidades de acordo com um padrão pré-definido. Elas podem ser encontradas na natureza, em animais sociais tais como peixes ou colónias de insetos, onde a organização espontânea em estruturas se verifica. As formações aplicam-se em diversos contextos, tais como cenários militares ou de aplicação da lei, onde são utilizadas para aumentar a performance operacional. O conceito está também presente em desportos coletivos tais como o futebol, onde as formações são utilizadas como estratégia para aumentar a eficiência das equipas. Os enxames de robots são uma abordagem para o estudo de sistemas multi-robô compostos de um grande número de unidades simples, inspirado na organização de sociedades animais. Estes têm um elevado potencial na resolução de tarefas demasiado complexas para um único robot. Quando aplicadas na coordenação deste tipo de sistemas, as formações permitem o movimento coordenado e o aumento da sensibilidade do enxame como um todo. Nesta dissertação apresentamos a síntese de controlo de formação para um sistema multi-robô. O controlo é sintetizado através do uso de robótica evolucionária, de onde o comportamento coletivo emerge, demonstrando ainda funcionalidadeschave tais como tolerância a falhas e robustez. As experiências iniciais na síntese de controlo foram realizadas em simulação. Mais tarde foi desenvolvida uma plataforma robótica para a condução de experiências no mundo real. Os nossos resultados demonstram que é possível sintetizar controlo de formação para um sistema multi-robô, utilizando técnicas de robótica evolucionária. A plataforma desenvolvida foi ainda utilizada em diversos estudos científicos

A Framework for Automatic Behavior Generation in Multi-Function Swarms

Multi-function swarms are swarms that solve multiple tasks at once. For example, a quadcopter swarm could be tasked with exploring an area of interest while simultaneously functioning as ad-hoc relays. With this type of multi-function comes the challenge of handling potentially conflicting requirements simultaneously. Using the Quality-Diversity algorithm MAP-elites in combination with a suitable controller structure, a framework for automatic behavior generation in multi-function swarms is proposed. The framework is tested on a scenario with three simultaneous tasks: exploration, communication network creation and geolocation of RF emitters. A repertoire is evolved, consisting of a wide range of controllers, or behavior primitives, with different characteristics and trade-offs in the different tasks. This repertoire would enable the swarm to transition between behavior trade-offs online, according to the situational requirements. Furthermore, the effect of noise on the behavior characteristics in MAP-elites is investigated. A moderate number of re-evaluations is found to increase the robustness while keeping the computational requirements relatively low. A few selected controllers are examined, and the dynamics of transitioning between these controllers are explored. Finally, the study develops a methodology for analyzing the makeup of the resulting controllers. This is done through a parameter variation study where the importance of individual inputs to the swarm controllers is assessed and analyzed

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

Learning Collaborative Foraging in a Swarm of Robots using Embodied Evolution

International audienceIn this paper, we study how a swarm of robots adapts over time to solve a collaborative task using a distributed Embodied Evolutionary approach , where each robot runs an evolutionary algorithm and they locally exchange genomes and fitness values. Particularly, we study a collabo-rative foraging task, where the robots are rewarded for collecting food items that are too heavy to be collected individually and need at least two robots to be collected. Further, the robots also need to display a signal matching the color of the item with an additional effector. Our experiments show that the distributed algorithm is able to evolve swarm behavior to collect items cooperatively. The experiments also reveal that effective cooperation is evolved due mostly to the ability of robots to jointly reach food items, while learning to display the right color that matches the item is done suboptimally. However, a closer analysis shows that, without a mechanism to avoid neglecting any kind of item, robots collect all of them, which means that there is some degree of learning to choose the right value for the color effector depending on the situation

Swarm robotics:design and implementation

This project presents a swarming and herding behaviour using simple robots. The main goal is to demonstrate the applicability of artificial intelligence (AI) in simple robotics that can then be scaled to industrial and consumer markets to further the ability of automation. AI can be achieved in many different ways; this paper explores the possible platforms on which to build a simple AI robots from consumer grade microcontrollers. Emphasis on simplicity is the main focus of this paper. Cheap and 8 bit microcontrollers were used as the brain of each robot in a decentralized swarm environment were each robot is autonomous but still a part of the whole. These simple robots don’t communicate directly with each other. They will utilize simple IR sensors to sense each other and simple limit switches to sense other obstacles in their environment. Their main objective is to assemble at certain location after initial start from random locations, and after converging they would move as a single unit without collisions. Using readily available microcontrollers and simple circuit design, semiconsistent swarming behaviour was achieved. These robots don’t follow a set path but will react dynamically to different scenarios, guided by their simple AI algorithm