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

Bio-inspired multi-agent systems for reconfigurable manufacturing systems

The current market’s demand for customization and responsiveness is a major challenge for producing intelligent, adaptive manufacturing systems. The Multi-Agent System (MAS) paradigm offers an alternative way to design this kind of system based on decentralized control using distributed, autonomous agents, thus replacing the traditional centralized control approach. The MAS solutions provide modularity, flexibility and robustness, thus addressing the responsiveness property, but usually do not consider true adaptation and re-configuration. Understanding how, in nature, complex things are performed in a simple and effective way allows us to mimic nature’s insights and develop powerful adaptive systems that able to evolve, thus dealing with the current challenges imposed on manufactur- ing systems. The paper provides an overview of some of the principles found in nature and biology and analyses the effectiveness of bio-inspired methods, which are used to enhance multi-agent systems to solve complex engineering problems, especially in the manufacturing field. An industrial automation case study is used to illustrate a bio-inspired method based on potential fields to dynamically route pallets

Computational aspects of cellular intelligence and their role in artificial intelligence.

The work presented in this thesis is concerned with an exploration of the computational aspects of the primitive intelligence associated with single-celled organisms. The main aim is to explore this Cellular Intelligence and its role within Artificial Intelligence. The findings of an extensive literature search into the biological characteristics, properties and mechanisms associated with Cellular Intelligence, its underlying machinery - Cell Signalling Networks and the existing computational methods used to capture it are reported. The results of this search are then used to fashion the development of a versatile new connectionist representation, termed the Artificial Reaction Network (ARN). The ARN belongs to the branch of Artificial Life known as Artificial Chemistry and has properties in common with both Artificial Intelligence and Systems Biology techniques, including: Artificial Neural Networks, Artificial Biochemical Networks, Gene Regulatory Networks, Random Boolean Networks, Petri Nets, and S-Systems. The thesis outlines the following original work: The ARN is used to model the chemotaxis pathway of Escherichia coli and is shown to capture emergent characteristics associated with this organism and Cellular Intelligence more generally. The computational properties of the ARN and its applications in robotic control are explored by combining functional motifs found in biochemical network to create temporal changing waveforms which control the gaits of limbed robots. This system is then extended into a complete control system by combining pattern recognition with limb control in a single ARN. The results show that the ARN can offer increased flexibility over existing methods. Multiple distributed cell-like ARN based agents termed Cytobots are created. These are first used to simulate aggregating cells based on the slime mould Dictyostelium discoideum. The Cytobots are shown to capture emergent behaviour arising from multiple stigmergic interactions. Applications of Cytobots within swarm robotics are investigated by applying them to benchmark search problems and to the task of cleaning up a simulated oil spill. The results are compared to those of established optimization algorithms using similar cell inspired strategies, and to other robotic agent strategies. Consideration is given to the advantages and disadvantages of the technique and suggestions are made for future work in the area. The report concludes that the Artificial Reaction Network is a versatile and powerful technique which has application in both simulation of chemical systems, and in robotic control, where it can offer a higher degree of flexibility and computational efficiency than benchmark alternatives. Furthermore, it provides a tool which may possibly throw further light on the origins and limitations of the primitive intelligence associated with cells