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 computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Exploiting automated technologies for reduction of rework in construction housing supply chain

Housing has been experiencing significant rework within the supply chain. Rework has afflicted both cost and schedule of projects due to the complex environment, intricate activities and highly fragmented nature of housing supply chain. Housing supply chain generate immense data and share information with different parties, which contribute to multitude of countless challenges. As a result of rework, productivity and workflow of information in construction supply chain has been affected with a catalogue of problems for the past few decades. Automation in construction supply chain with novel technological and analytical strategies has aspired industry to improve the productivity and change the trajectory of traditional, manual and analogue way of processing. The aim of this study is to explore possible opportunities of employing new technologies and challenges involved in utilising automated technologies for minimising rework in housing supply chain. The research methodology is based on a review of literature to investigate automated technologies to eliminate rework in housing supply chain. A conceptual framework is proposed to determine the suitability of various technologies to fully automate housing supply chain and facilitate the reduction of rework in construction housing supply chain. All rights are reserved for Diamond Congress Ltd., Budapest, Hungary, except the right of the authors to (re)publish their materials wherever they decide. This book is a working material for the Creative Construction Conference 201

Application of Artificial Intelligence in Automation of Supply Chain Management

A well-functioning supply chain is a key to success for every business entity. Having an accurate projection on inventory offers a substantial competitive advantage. There are many internal factors like product introductions, distribution network expansion; and external factors such as weather, extreme seasonality, and changes in customer perception or media coverage that affects the performance of the supply chain. In recent years Artificial Intelligence (AI) has been proved to become an extension of our brain, expanding our cognitive abilities to levels that we never thought would be possible. Though many believe AI will replace humans, it is not true, rather it will help us to unleash our true strategic and creative potential. AI consists of a set of computational technologies developed to sense, learn, reason, and act appropriately. With the technological advancement in mobile computing, the capacity to store huge data on the internet, cloud-based machine learning and information processing algorithms etc. AI has been integrated into many sectors of business and been proved to reduce costs, increase revenue, and enhance asset utilization. AI is helping businesses to get almost 100% accurate projection and forecast the customer demand, optimizing their R&D and increase manufacturing with lower cost and higher quality, helping them in the promotion (identifying target customers, demography, defining the price, and designing the right message, etc.) and providing their customers a better experience. These four areas of value creation are extremely important for gaining competitive advantage. Supply-chain leaders use AI-powered technologies to a) make efficient designs to eliminate waste b) real-time monitoring and error-free production and c) facilitate lower process cycle times. These processes are crucial in bringing Innovation faster to the market

Novel approaches to cooperative coevolution of heterogeneous multiagent systems

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2017Heterogeneous multirobot systems are characterised by the morphological and/or behavioural heterogeneity of their constituent robots. These systems have a number of advantages over the more common homogeneous multirobot systems: they can leverage specialisation for increased efficiency, and they can solve tasks that are beyond the reach of any single type of robot, by combining the capabilities of different robots. Manually designing control for heterogeneous systems is a challenging endeavour, since the desired system behaviour has to be decomposed into behavioural rules for the individual robots, in such a way that the team as a whole cooperates and takes advantage of specialisation. Evolutionary robotics is a promising alternative that can be used to automate the synthesis of controllers for multirobot systems, but so far, research in the field has been mostly focused on homogeneous systems, such as swarm robotics systems. Cooperative coevolutionary algorithms (CCEAs) are a type of evolutionary algorithm that facilitate the evolution of control for heterogeneous systems, by working over a decomposition of the problem. In a typical CCEA application, each agent evolves in a separate population, with the evaluation of each agent depending on the cooperation with agents from the other coevolving populations. A CCEA is thus capable of projecting the large search space into multiple smaller, and more manageable, search spaces. Unfortunately, the use of cooperative coevolutionary algorithms is associated with a number of challenges. Previous works have shown that CCEAs are not necessarily attracted to the global optimum, but often converge to mediocre stable states; they can be inefficient when applied to large teams; and they have not yet been demonstrated in real robotic systems, nor in morphologically heterogeneous multirobot systems. In this thesis, we propose novel methods for overcoming the fundamental challenges in cooperative coevolutionary algorithms mentioned above, and study them in multirobot domains: we propose novelty-driven cooperative coevolution, in which premature convergence is avoided by encouraging behavioural novelty; and we propose Hyb-CCEA, an extension of CCEAs that places the team heterogeneity under evolutionary control, significantly improving its scalability with respect to the team size. These two approaches have in common that they take into account the exploration of the behaviour space by the evolutionary process. Besides relying on the fitness function for the evaluation of the candidate solutions, the evolutionary process analyses the behaviour of the evolving agents to improve the effectiveness of the evolutionary search. The ultimate goal of our research is to achieve general methods that can effectively synthesise controllers for heterogeneous multirobot systems, and therefore help to realise the full potential of this type of systems. To this end, we demonstrate the proposed approaches in a variety of multirobot domains used in previous works, and we study the application of CCEAs to new robotics domains, including a morphological heterogeneous system and a real robotic system.Fundação para a Ciência e a Tecnologia (FCT, PEst-OE/EEI/LA0008/2011

Umjetna inteligencija i robotika kao pokretačka snaga modernog društva

In synergy with other technologies, the AI significantly accelerates the scientific and technological development of human society. New possibilities of the application of technological achievements are constantly opening up – in industry, healthcare and everyday life. AI-based robotics is the main driver of the present industrial revolution. Robots have already played an important role in production and changed the production economy over the past decade. New generations of smart robots, or smart technical systems in general, are turning to new applications, especially in service industries, medicine and home use. In the future, autonomous and mobile robots will be able to assist the elderly and immobile, help with household chores, act as caregivers and perform repetitive, tedious or dangerous jobs in nursing homes, hospitals, military environments, disaster sites and schools. The potential benefits are great, but they pose significant ethical challenges too. Our autonomy may be compromised and social interaction obstructed. Expanded use of robots can lead to reduced contact among people and possible restrictions on personal freedoms. Machines of these kinds shape the new world radically, leading to significant economic and cultural changes, creating both winners and losers on a global scale.U sinergiji s drugim tehnologijama AI značajno ubrzava znanstveni i tehnološki razvoj ljudskog društva. Neprestano se otvaraju nove mogućnosti primjene tehnoloških dostignuća, kako u industriji, zdravstvu tako i u svakodnevnom životu. Robotika temeljena na umjetnoj inteligenciji glavni je pokretač sadašnje industrijske revolucije. Roboti su već odigrali važnu ulogu u proizvodnji i promijenili proizvodnu ekonomiju tijekom posljednjih desetak godina. Nove generacije pametnih robota, ili općenito pametnih tehničkih sustava, okreću se novim primjenama, posebno u uslužnim djelatnostima, medicini i kućnoj uporabi. Autonomni i mobilni roboti u budućnosti će moći pomagati starijim i nepokretnim osobama, pomagati u kućanskim poslovima, djelovati kao njegovatelji i obavljati ponavljajuće, dosadne ili opasne poslove u staračkim domovima, bolnicama, vojnim okruženjima, mjestima katastrofe i školama. Potencijalne prednosti su velike, ali također predstavljaju značajne etičke izazove. Naša autonomija može biti ugrožena, a društvena interakcija opstruirana. Prošireno korištenje robota može dovesti do smanjenog kontakta među ljudima i mogućih ograničenja osobnih sloboda. Strojevi ove vrste oblikuju radikalno novi svijet, što dovodi do značajnih ekonomskih i kulturoloških promjena, stvarajući jednako pobjednike kao i gubitnike na globalnoj svjetskoj razini

A Systematic Literature Review of Quantum Computing for Routing Problems

Quantum Computing is drawing a significant attention from the current scientific community. The potential advantages offered by this revolutionary paradigm has led to an upsurge of scientific production in different fields such as economics, industry, or logistics. The main purpose of this paper is to collect, organize and systematically examine the literature published so far on the application of Quantum Computing to routing problems. To do this, we embrace the well-established procedure named as Systematic Literature Review. Specifically, we provide a unified, self-contained, and end-to-end review of 18 years of research (from 2004 to 2021) in the intersection of Quantum Computing and routing problems through the analysis of 53 different papers. Several interesting conclusions have been drawn from this analysis, which has been formulated to give a comprehensive summary of the current state of the art by providing answers related to the most recurrent type of study (practical or theoretical), preferred solving approaches (dedicated or hybrid), detected open challenges or most used Quantum Computing device, among others

Decentralized Decision Making for Limited Resource Allocation Using a Private Blockchain Network in an IoT (Internet of Things) Environment with Conflicting Agents

Blockchains have gotten popular in recent times, owing to the security, anonymity, and lack of any third-party involvement. Blockchains essentially are record keeping tools that record any transactions between involved parties. One of the key aspects of handling and navigating of any autonomous traffic on the streets, is secured and simple means of communication. This thesis explores distribution of minimum resources between multiple autonomous agents, by settling conflicts using events of random nature. The thesis focusses on two specific events, tossing of a coin and the game of rock, paper, and scissors (RPS). An improvement on the traditional game of RPS is further suggested, called rock, paper, scissors, and hammer (RPSH). And then seamless communication interface to enable secure interaction is setup using blockchains with smart contracts. A new method of information exchange called Sealed Envelope Exchange is proposed to eliminate any involvement of third-party agents in the monitoring of conflict resolution. A scenario of assigning the sole remaining parking spot in a filled parking space, between two vehicles is simulated and then the conflict is resolved in a fair manner without involving a third-party agent. This is achieved by playing a fair game of RPSH by using blockchains and simulating cross chain interaction to ensure that any messages and transactions during the game are secured