TEO robot design powered by a fuel cell system

Versión pre-print (sin revisión por pares) del artículo publicado en Cybernetics and Systems: An International Journal (2012), 43(3), 163-180, accesible en linea: http://dx.doi.org/10.1080/01969722.2012.659977.This is an Author's Original Manuscript (non-peer reviewed) of an article published in Cybernetics and Systems: An International Journal (2012), 43(3), 163-180, available online: http://dx.doi.org/10.1080/01969722.2012.659977.This article deals with the design of the full-size humanoid robot TEO, an improved version of its predecessor Rh-1. The whole platform is conceived under the premise of high efficiency in terms of energy consumption and optimization. We will focus mainly on the electromechanical structure of the lower part of the prototype, which is the main component demanding energy during motion. The dimensions and weight of the robotic platform, together with its link configuration and rigidity, will be optimized. Experimental results are presented to show the validity of the design.The research leading to these results has received funding from the RoboCity2030-II-CM project (S2009/DPI-1559), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

Analysis and design of Multi-Agent Coverage and Transport algorithms

Els sistemes robòtics multi-agents són sistemes que presenten moltes aplicacions en ciència i enginyeria. En aquest treball estudiarem el control de la cobertura, que es centra en col·locar un grup de sensors per optimitzar la cobertura d’una densitat. Ens centrarem en el cas en què la densitat evoluciona en el temps i estudiarem l’ús de la teoría de perturbacions singulars per resoldre el problema. També considerarem grans eixams de robots, on podem fer servir models continus per analitzar el comportament dels agents. Recentment s'ha proposat models continus que incorporen idees de transport òptim en el problema de transport multi-agent. Presentarem aquests treballs i proveirem algunes modificacions.Los sistemas robóticos multi-agentes son sistemas que presentan muchas aplicaciones en ciencia y ingeniería. En este trabajo vamos a estudiar el control de la cobertura, que se centra en colocar un grupo de sensores para optimizar la cobertura de una densidad. Nos vamos a centrar en el casos en que la densidad evoluciona con el tiempo y estudiaremos el uso de la teoría de perturbaciones singulares para resolver el problema. También consideraremos grandes enjambres de robots, donde podemos utilizar modelos continuos para analizar el comportamiento del enjambre. Recientemente se ha propuesto el uso de modelos continuos que incorporan ideas de transporte òptimo para el problema de transporte multi-agente. Vamos a presentar dichos trabajos y proveeremos algunas modificaciones.Multi-agent robotic systems have shown to be useful and reliable solutions to many problems that arise in science and engineering. In this work we will study Coverage Control, that aims to achieve optimal coverage of a density. We will focus on the case when the density has a time dependence and we will study a Singular Perturbation Theory approach to solve the problem. We will also consider large swarms of agents, where we can develop continuous models to analyze the behaviour of the swarm. Recent work has focused on applying ideas from the theory of Optimal Transport to the Multi-Agent Transport problem. We will review the work and provide some modifications.Outgoin

Marsupial 로봇 팀의 효율적인 배치 및 회수를 위한 경로 계획에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 공과대학 전기·컴퓨터공학부, 2017. 8. 이범희.This dissertation presents time-efficient approaches to path planning for initial deployment and collection of a heterogeneous marsupial robot team consists of a large-scale carrier robot and multiple mobile robots. Although much research has been conducted about task allocation and path planning of multi-robot systems, the path planning problems for deployment and collection of a marsupial robot team have not been fully addressed. The objectives of the problems are to minimize the duration that mobile robots require to reach their assigned task locations and return from those locations. Taking the small mobile robot's energy constraint into account, a large-scale carrier robot, which is faster than a mobile robot, is considered for efficient deployment and collection. The carrier robot oversees transporting, deploying, and retrieving of mobile robots, whereas the mobile robots are responsible for carrying out given tasks such as reconnaissance and search and rescue. The path planning methods are introduced in both an open space without obstacles and a roadmap graph which avoids obstacles. For the both cases, determining optimal path requires enormous search space whose computational complexity is equivalent to solving a combinatorial optimization problem. To reduce the amount of computation, both task locations and mobile robots to be collected are divided into a number of clusters according to their geographical adjacency and their energies. Next, the cluster are sorted based on the location of the carrier robot. Then, an efficient path for the carrier robot can be generated by traveling to each deploying and loading location relevant to each cluster. The feasibility of the proposed algorithms is demonstrated through several simulations in various environments including three-dimensional space and dynamic task environment. Finally, the performance of the proposed algorithms is also demonstrated by comparing with other simple methods.Chapter 1 Introduction 1 1.1 Background and motivation 1 1.1.1 Multi-robot system 1 1.1.2 Marsupial robot team 3 1.2 Contributions of the thesis 9 Chapter 2 Related Work 13 2.1 Multi-robot path planning 14 2.2 Multi-robot exploration 14 2.3 Multi-robot task allocation 15 2.4 Simultaneous localization and mapping 15 2.5 Motion planning of collective swarm 16 2.6 Marsupial robot team 18 2.6.1 Multi-robot deployment 18 2.6.2 Marsupial robot 19 2.7 Robot collection 23 2.8 Roadmap generation 24 2.8.1 Geometric algorithms 24 2.8.2 Sampling-based algorithms 25 2.9 Novelty of the thesis 26 Chapter 3 Preliminaries 27 3.1 Notation 27 3.2 Assumptions 29 3.3 Clustering algorithm 30 3.4 Minimum bounded circle and sphere of a cluster 32 Chapter 4 Deployment of a Marsupial Robot Team 35 4.1 Problem definition 35 4.2 Complexity analysis 37 4.3 Optimal deployment path planning for two tasks 38 4.3.1 Deployment for two tasks in 2D space 39 4.3.2 Deployment for two tasks in 3D space 41 4.4 Path planning algorithm of a marsupial robot team for deployment 42 4.5 Simulation result 49 4.5.1 Simulation setup 49 4.5.2 Deployment scenarios in 2D space 50 4.5.3 Deployment scenarios in 3D space 57 4.5.4 Deployment in a dynamic environment 60 4.6 Performance evaluation 62 4.6.1 Computation time 62 4.6.2 Efficiency of the path 64 Chapter 5 Collection of a Marsupial Robot Team 67 5.1 Problem definition 68 5.2 Complexity analysis 70 5.3 Optimal collection path planning for two rovers 71 5.3.1 Collection for two rovers in 2D space 71 5.3.2 Collection for two rovers in 3D space 75 5.4 Path planning algorithm of a marsupial robot team for collection 76 5.5 Simulation result 83 5.5.1 Collection scenarios in 2D space 83 5.5.2 Collection scenarios in 3D space 88 5.5.3 Collection in a dynamic environment 91 5.6 Performance evaluation 93 5.6.1 Computation time 93 5.6.2 Efficiency of the path 95 Chapter 6 Deployment of a Marsupial Robot Team using a Graph 99 6.1 Problem definition 99 6.2 Framework 101 6.3 Probabilistic roadmap generation 102 6.3.1 Global PRM 103 6.3.2 Local PRM 105 6.4 Path planning strategy 105 6.4.1 Clustering scheme 106 6.4.2 Determining deployment locations 109 6.4.3 Path smoothing 113 6.4.4 Path planning algorithm for a marsupial robot team 115 6.5 Simulation result 116 6.5.1 Outdoor space without obstacle 116 6.5.2 Outdoor space with obstacles 118 6.5.3 Office area 119 6.5.4 University research building 122 Chapter 7 Conclusion 125 Bibliography 129 초록 151Docto

SWARM INTELLIGENCE AND STIGMERGY: ROBOTIC IMPLEMENTATION OF FORAGING BEHAVIOR

Swarm intelligence in multi-robot systems has become an important area of research within collective robotics. Researchers have gained inspiration from biological systems and proposed a variety of industrial, commercial, and military robotics applications. In order to bridge the gap between theory and application, a strong focus is required on robotic implementation of swarm intelligence. To date, theoretical research and computer simulations in the field have dominated, with few successful demonstrations of swarm-intelligent robotic systems. In this thesis, a study of intelligent foraging behavior via indirect communication between simple individual agents is presented. Models of foraging are reviewed and analyzed with respect to the system dynamics and dependence on important parameters. Computer simulations are also conducted to gain an understanding of foraging behavior in systems with large populations. Finally, a novel robotic implementation is presented. The experiment successfully demonstrates cooperative group foraging behavior without direct communication. Trail-laying and trail-following are employed to produce the required stigmergic cooperation. Real robots are shown to achieve increased task efficiency, as a group, resulting from indirect interactions. Experimental results also confirm that trail-based group foraging systems can adapt to dynamic environments

Evolutionary Robot Swarms Under Real-World Constraints

Tese de doutoramento em Engenharia Electrotécnica e de Computadores, na especialidade de Automação e Robótica, apresentada ao Departamento de Engenharia Electrotécnica e de Computadores da Faculdade de Ciências e Tecnologia da Universidade de CoimbraNas últimas décadas, vários cientistas e engenheiros têm vindo a estudar as estratégias provenientes da natureza. Dentro das arquiteturas biológicas, as sociedades que vivem em enxames revelam que agentes simplistas, tais como formigas ou pássaros, são capazes de realizar tarefas complexas usufruindo de mecanismos de cooperação. Estes sistemas abrangem todas as condições necessárias para a sobrevivência, incorporando comportamentos de cooperação, competição e adaptação. Na “batalha” sem fim em prol do progresso dos mecanismos artificiais desenvolvidos pelo homem, a ciência conseguiu simular o primeiro comportamento em enxame no final dos anos oitenta. Desde então, muitas outras áreas, entre as quais a robótica, beneficiaram de mecanismos de tolerância a falhas inerentes da inteligência coletiva de enxames. A área de investigação deste estudo incide na robótica de enxame, consistindo num domínio particular dos sistemas robóticos cooperativos que incorpora os mecanismos de inteligência coletiva de enxames na robótica. Mais especificamente, propõe-se uma solução completa de robótica de enxames a ser aplicada em contexto real. Nesta ótica, as operações de busca e salvamento foram consideradas como o caso de estudo principal devido ao nível de complexidade associado às mesmas. Tais operações ocorrem tipicamente em cenários dinâmicos de elevadas dimensões, com condições adversas que colocam em causa a aplicabilidade dos sistemas robóticos cooperativos. Este estudo centra-se nestes problemas, procurando novos desafios que não podem ser ultrapassados através da simples adaptação da literatura da especialidade em algoritmos de enxame, planeamento, controlo e técnicas de tomada de decisão. As contribuições deste trabalho sustentam-se em torno da extensão do método Particle Swarm Optimization (PSO) aplicado a sistemas robóticos cooperativos, denominado de Robotic Darwinian Particle Swarm Optimization (RDPSO). O RDPSO consiste numa arquitetura robótica de enxame distribuída que beneficia do particionamento dinâmico da população de robôs utilizando mecanismos evolucionários de exclusão social baseados na sobrevivência do mais forte de Darwin. No entanto, apesar de estar assente no caso de estudo do RDPSO, a aplicabilidade dos conceitos aqui propostos não se encontra restrita ao mesmo, visto que todos os algoritmos parametrizáveis de enxame de robôs podem beneficiar de uma abordagem idêntica. Os fundamentos em torno do RDPSO são introduzidos, focando-se na dinâmica dos robôs, nos constrangimentos introduzidos pelos obstáculos e pela comunicação, e nas suas propriedades evolucionárias. Considerando a colocação inicial dos robôs no ambiente como algo fundamental para aplicar sistemas de enxames em aplicações reais, é assim introduzida uma estratégia de colocação de robôs realista. Para tal, a população de robôs é dividida de forma hierárquica, em que são utilizadas plataformas mais robustas para colocar as plataformas de enxame no cenário de forma autónoma. Após a colocação dos robôs no cenário, é apresentada uma estratégia para permitir a criação e manutenção de uma rede de comunicação móvel ad hoc com tolerância a falhas. Esta estratégia não considera somente a distância entre robôs, mas também a qualidade do nível de sinal rádio frequência, redefinindo assim a sua aplicabilidade em cenários reais. Os aspetos anteriormente mencionados estão sujeitos a uma análise detalhada do sistema de comunicação inerente ao algoritmo, para atingir uma implementação mais escalável do RDPSO a cenários de elevada complexidade. Esta elevada complexidade inerente à dinâmica dos cenários motivaram a ultimar o desenvolvimento do RDPSO, integrando para o efeito um mecanismo adaptativo baseado em informação contextual (e.g., nível de atividade do grupo). Face a estas considerações, o presente estudo pode contribuir para expandir o estado-da-arte em robótica de enxame com algoritmos inovadores aplicados em contexto real. Neste sentido, todos os métodos propostos foram extensivamente validados e comparados com alternativas, tanto em simulação como com robôs reais. Para além disso, e dadas as limitações destes (e.g., número limitado de robôs, cenários de dimensões limitadas, constrangimentos reais limitados), este trabalho contribui ainda para um maior aprofundamento do estado-da-arte, onde se propõe um modelo macroscópico capaz de capturar a dinâmica inerente ao RDPSO e, até certo ponto, estimar analiticamente o desempenho coletivo dos robôs perante determinada tarefa. Em suma, esta investigação pode ter aplicabilidade prática ao colmatar a lacuna que se faz sentir no âmbito das estratégias de enxames de robôs em contexto real e, em particular, em cenários de busca e salvamento.Over the past decades, many scientists and engineers have been studying nature’s best and time-tested patterns and strategies. Within the existing biological architectures, swarm societies revealed that relatively unsophisticated agents with limited capabilities, such as ants or birds, were able to cooperatively accomplish complex tasks necessary for their survival. Those simplistic systems embrace all the conditions necessary to survive, thus embodying cooperative, competitive and adaptive behaviours. In the never-ending battle to advance artificial manmade mechanisms, computer scientists simulated the first swarm behaviour designed to mimic the flocking behaviour of birds in the late eighties. Ever since, many other fields, such as robotics, have benefited from the fault-tolerant mechanism inherent to swarm intelligence. The area of research presented in this Ph.D. Thesis focuses on swarm robotics, which is a particular domain of multi-robot systems (MRS) that embodies the mechanisms of swarm intelligence into robotics. More specifically, this Thesis proposes a complete swarm robotic solution that can be applied to real-world missions. Although the proposed methods do not depend on any particular application, search and rescue (SaR) operations were considered as the main case study due to their inherent level of complexity. Such operations often occur in highly dynamic and large scenarios, with harsh and faulty conditions, that pose several problems to MRS applicability. This Thesis focuses on these problems raising new challenges that cannot be handled appropriately by simple adaptation of state-of-the-art swarm algorithms, planning, control and decision-making techniques. The contributions of this Thesis revolve around an extension of the Particle Swarm Optimization (PSO) to MRS, denoted as Robotic Darwinian Particle Swarm Optimization (RDPSO). The RDPSO is a distributed swarm robotic architecture that benefits from the dynamical partitioning of the whole swarm of robots by means of an evolutionary social exclusion mechanism based on Darwin’s survival-of-the-fittest. Nevertheless, although currently applied solely to the RDPSO case study, the applicability of all concepts herein proposed is not restricted to it, since all parameterized swarm robotic algorithms may benefit from a similar approach The RDPSO is then proposed and used to devise the applicability of novel approaches. The fundamentals around the RDPSO are introduced by focusing on robots’ dynamics, obstacle avoidance, communication constraints and its evolutionary properties. Afterwards, taking the initial deployment of robots within the environment as a basis for applying swarm robotics systems into real-world applications, the development of a realistic deployment strategy is proposed. For that end, the population of robots is hierarchically divided, wherein larger support platforms autonomously deploy smaller exploring platforms in the scenario, while considering communication constraints and obstacles. After the deployment, a way of ensuring a fault-tolerant multi-hop mobile ad hoc communication network (MANET) is introduced to explicitly exchange information needed in a collaborative realworld task execution. Such strategy not only considers the maximum communication range between robots, but also the minimum signal quality, thus refining the applicability to real-world context. This is naturally followed by a deep analysis of the RDPSO communication system, describing the dynamics of the communication data packet structure shared between teammates. Such procedure is a first step to achieving a more scalable implementation by optimizing the communication procedure between robots. The highly dynamic characteristics of real-world applications motivated us to ultimate the RDPSO development with an adaptive strategy based on a set of context-based evaluation metrics. This thesis contributes to the state-of-the-art in swarm robotics with novel algorithms for realworld applications. All of the proposed approaches have been extensively validated in benchmarking tasks, in simulation, and with real robots. On top of that, and due to the limitations inherent to those (e.g., number of robots, scenario dimensions, real-world constraints), this Thesis further contributes to the state-of-the-art by proposing a macroscopic model able to capture the RDPSO dynamics and, to some extent, analytically estimate the collective performance of robots under a certain task. It is the author’s expectation that this Ph.D. Thesis may shed some light into bridging the reality gap inherent to the applicability of swarm strategies to real-world scenarios, and in particular to SaR operations.FCT - SFRH/BD /73382/201