11 research outputs found
Effective Cooperation and Scalability in Multi-Robot Teams for Automatic Patrolling of Infrastructures
Tese de doutoramento em Engenharia Electrotécnica e de Computadores, apresentada ao Departamento de Engenharia Electrotécnica e de Computadores da Faculdade de Ciências e Tecnologia da Universidade de CoimbraIn the digital era that we live in, advances in technology have proliferated throughout our society, quickening the completion of tasks that were painful in the old days, improving solutions to the everyday problems that we face, and generally assisting human beings both in their professional and personal life. Robotics is a clear example of a broad technological field that evolves every day. In fact, scientists predict that in the upcoming few decades, robots will naturally interact and coexist alongside human beings.
While it is true that robots already have a strong presence in industrial environments, e.g., robotic arms for manufacturing, the average person still looks upon robots with suspicion, since they are not acquainted by such type of technology. In this thesis, the author deploys teams of mobile robots in indoor scenarios to cooperatively perform patrolling missions, which represents an effort to bring robots closer to humans and assist them in monotonous or repetitive tasks, such as supervising and monitoring indoor infrastructures or simply cooperatively cleaning floors.
In this context, the team of robots should be able to sense the environment, localize and navigate autonomously between way points while avoiding obstacles, incorporate any number of robots, communicate actions in a distributed way and being robust not only to agent failures but also communication failures, so as to effectively coordinate to achieve optimal collective performance. The referred capabilities are an evidence that such systems can only prove their reliability in real-world environments if robots are endowed with intelligence and autonomy. Thus, the author follows a line of research where patrolling units have the necessary tools for intelligent decision-making, according to the information of the mission, the environment and teammates' actions, using distributed coordination architectures.
An incremental approach is followed. Firstly, the problem is presented and the literature is deeply studied in order to identify potential weaknesses and research opportunities, backing up the objectives and contributions proposed in this thesis. Then, problem fundamentals are described and benchmarking of multi-robot patrolling algorithms in realistic conditions is conducted. In these earlier stages, the role of different parameters of the problem, like environment connectivity, team size and strategy philosophy, will become evident through extensive empirical results and statistical analysis. In addition, scalability is deeply analyzed and tied with inter-robot interference and coordination, imposed by each patrolling strategy.
After gaining sensibility to the problem, preliminary models for multi-robot patrol with special focus on real-world application are presented, using a Bayesian inspired formalism. Based on these, distributed strategies that lead to superior team performance are described. Interference between autonomous agents is explicitly dealt with, and the approaches are shown to scale to large teams of robots. Additionally, the robustness to agent and communication failures is demonstrated, as well as the flexibility of the model proposed. In fact, by later generalizing the model with learning agents and maintaining memory of past events, it is then shown that these capabilities can be inherited, while at the same time increasing team performance even further and fostering adaptability. This is verified in simulation experiments and real-world results in a large indoor scenario.
Furthermore, since the issue of team scalability is highly in focus in this thesis, a method for estimating the optimal team size in a patrolling mission, according to the environment topology is proposed. Upper bounds for team performance prior to the mission start are provided, supporting the choice of the number of robots to be used so that temporal constraints can be satisfied.
All methods developed in this thesis are tested and corroborated by experimental results, showing the usefulness of employing cooperative teams of robots in real-world environments and the potential for similar systems to emerge in our society.FCT - SFRH/BD/64426/200
A System for Deduction-based Formal Verification of Workflow-oriented Software Models
The work concerns formal verification of workflow-oriented software models
using deductive approach. The formal correctness of a model's behaviour is
considered. Manually building logical specifications, which are considered as a
set of temporal logic formulas, seems to be the significant obstacle for an
inexperienced user when applying the deductive approach. A system, and its
architecture, for the deduction-based verification of workflow-oriented models
is proposed. The process of inference is based on the semantic tableaux method
which has some advantages when compared to traditional deduction strategies.
The algorithm for an automatic generation of logical specifications is
proposed. The generation procedure is based on the predefined workflow patterns
for BPMN, which is a standard and dominant notation for the modeling of
business processes. The main idea for the approach is to consider patterns,
defined in terms of temporal logic,as a kind of (logical) primitives which
enable the transformation of models to temporal logic formulas constituting a
logical specification. Automation of the generation process is crucial for
bridging the gap between intuitiveness of the deductive reasoning and the
difficulty of its practical application in the case when logical specifications
are built manually. This approach has gone some way towards supporting,
hopefully enhancing our understanding of, the deduction-based formal
verification of workflow-oriented models.Comment: International Journal of Applied Mathematics and Computer Scienc
MRsensing: environmental monitoring and context recognition with cooperative mobile robots in catastrophic incidents
Dissertação de Mestrado em Engenharia Electrotécnica e de Computadores, apresentada à Faculdade de Ciências e Tecnologia da Universidade de CoimbraMulti-sensor information fusion theory concerns the environmental perception activities
to combine data from multiple sensory resources. Humans, as any other animals, gather
information from the environment around them using different biological sensors. Combining
them allows structuring the decisions and actions when interacting with the environment.
Under disaster conditions, effective mult-robot information sensor fusion can
yield a better situation awareness to support the collective decision-making. Mobile robots
can gather information from the environment by combining data from different sensors
as a way to organize decisions and augment human perception. The is especially useful
to retrieve contextual environmental information in catastrophic incidents where human
perception may be limited (e.g., lack of visibility). To that end, this work proposes a
specific configuration of sensors assembled in a mobile robot, which can be used as a
proof of concept to measure important environmental variables in an urban search and
rescue (USAR) mission, such as toxic gas density, temperature gradient and smoke particles
density. This data is processed through a support vector machine classifier with the
purpose of detecting relevant contexts in the course of the mission. The outcome provided
by the experiments conducted with TraxBot and Pioneer-3DX robots under the Robot
Operating System framework opens the door for new multi-robot applications on USAR
scenarios. This work was developed within the CHOPIN research project1 which aims at
exploiting the cooperation between human and robotic teams in catastrophic accidents.O tema da fusão sensorial abrange a perceção ambiental para combinar dados de vários recursos
naturais. Os seres humanos, como todos os outros animais, recolhem informações
do seu redor, utilizando diferentes sensores biológicos. Combinando-se informação dos
diferentes sensores é possível estruturar decisões e ações ao interagir com o meio ambiente.
Sob condições de desastres, a fusão sensorial de informação eficaz proveniente de
múltiplos robôs pode levar a um melhor reconhecimento da situação para a tomada de
decisão coletiva. Os robôs móveis podem extrair informações do ambiente através da combinação
de dados de diferentes sensores, como forma de organizar as decisões e aumentar
a perceção humana. Isto é especialmente útil para obter informações de contexto ambientais
em cenários de catástrofe, onde a perceção humana pode ser limitada (por exemplo,
a falta de visibilidade). Para este fim, este trabalho propõe uma configuração específica
de sensores aplicados num robô móvel, que pode ser usado como prova de conceito
para medir variáveis ambientais importantes em missões de busca e salvamento urbano
(USAR), tais como a densidade do gás tóxico, gradiente de temperatura e densidade de
partículas de fumo. Esta informação é processada através de uma máquina de vetores
de suporte com a finalidade de classificar contextos relevantes no decorrer da missão. O
resultado fornecido pelas experiências realizadas com os robôs TraxBot e Pioneer 3DX
usando a arquitetura Robot Operating System abre a porta para novas aplicações com
múltiplos robôs em cenários USAR
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
Application of Machine Learning to Financial Time Series Analysis
This multidisciplinary thesis investigates the application of machine learning to financial time series analysis. The research is motivated by the following thesis question: ‘Can one improve upon the state of the art in financial time series analysis through the application of machine learning?’ The work is split according to the following time series trichotomy: 1) characterization — determine the fundamental properties of the time series; 2) modelling — find a description that accurately captures features of the long-term behaviour of the system; and 3) forecasting — accurately predict the short-term evolution of the system
Contributions to Monte Carlo Search
This research is motivated by improving decision making under uncertainty and in particular for games and symbolic regression. The present dissertation gathers research contributions in the field of Monte Carlo Search. These contributions are focused around the selection, the simulation and the recommendation policies. Moreover, we develop a methodology to automatically generate an MCS algorithm for a given problem.
For the selection policy, in most of the bandit literature, it is assumed that there is no structure or similarities between arms. Thus each arm is independent from one another. In several instances however, arms can be closely related. We show both theoretically and empirically, that a significant improvement over the state-of-the-art selection policies is possible.
For the contribution on simulation policy, we focus on the symbolic regression problem and ponder on how to consistently generate different expressions by changing the probability to draw each symbol. We formalize the situation into an optimization problem and try different approaches. We show a clear improvement in the sampling process for any length. We further test the best approach by embedding it into a MCS algorithm and it still shows an improvement.
For the contribution on recommendation policy, we study the most common in combination with selection policies. A good recommendation policy is a policy that works well with a given selection policy. We show that there is a trend that seems to favor a robust recommendation policy over a riskier one.
We also present a contribution where we automatically generate several MCS algorithms from a list of core components upon which most MCS algorithms are built upon and compare them to generic algorithms. The results show that it often enables discovering new variants of MCS that significantly outperform generic MCS algorithms
Beyond the Frontiers of Timeline-based Planning
Any agent, either biological or artificial, understands how to behave in its environment according to its prior knowledge and to its prior experience. The process of deciding which actions to undertake and how to perform them so as to achieve some desired objective is called deliberation. In particular, planning is an abstract and explicit deliberation process that chooses and organizes actions, by anticipating their expected outcomes, with the aim to achieve, as best as possible, some pre-stated objectives called goals. Among the most widespread approaches to automated planning, the classical approach broadly pursues to the following definition of planning: starting from a description of the initial state of the world, a description of the desired goals, and a description of a set of possible actions, the planning problem consists in synthesizing a plan, i.e., a sequence of actions, that is guaranteed, when applied to the initial state, to generate a state, called a goal state, which contains the desired goals.
In order to cope with computational complexity, however, the classical approach to planning introduces some restrictive assumptions. Among them, for example, there is no explicit model of time and concurrency is treated only roughly. Additionally, goals are specified as a set of goal states, therefore, objectives such as states to be avoided and constraints on state trajectories or utility functions are not handled. In order to relax these restrictions, some alternative approaches have been proposed over the years. The timeline-based approach to planning, in particular, represents an effective alternative to classical planning for complex domains requiring the use of both temporal reasoning and scheduling features. This thesis focuses on timeline-based planning, aiming at solving some efficiency issues which inevitably raise as a consequence of the drop out of these restrictions. Regardless of the followed approach, indeed, it turns out that automated planning is a rather complex task from a computational point of view. Furthermore, not all of the approaches proposed in literature can rely on effective heuristics for efficiently tackling the search. This is particularly true in the case of the more recent and hence less investigated timeline-based formulation. Most of the timeline-based planners, in particular, have usually neglected the advantages triggered in classical planning from the use of Graphplan and/or modern heuristic search, namely the capability of reasoning on the whole domain model. This thesis aims at reducing the performance gap between the classical approach at planning and the timeline-based one. Specifically, the overall goal is to improve the efficiency of timeline-based reasoners taking inspiration from techniques applied in more classical approaches to planning. The main contributions of this thesis, therefore, are a) a new formalism for timeline-based planning which overcomes some limitations of the existing ones; b) a set of heuristics, inspired by the classical approach, that improve the performance of the timeline-based approach to planning; c) the introduction of sophisticated techniques like the non-chronological backtracking and the no-good learning, commonly used in other fields such as Constraint Processing, into the search process;d) the reorganization of the existing solver architectures, of a new solver called ORATIO, that allows to push the reasoning process beyond the sole automated planning, winking at emerging fields like, for example, Explainable AI and e) the introduction of a new language for expressing timeline-based planning problems called RIDDLE
PRESTK : situation-aware presentation of messages and infotainment content for drivers
The amount of in-car information systems has dramatically increased over the last few years. These potentially mutually independent information systems presenting information to the driver increase the risk of driver distraction. In a first step, orchestrating these information systems using techniques from scheduling and presentation planning avoid conflicts when competing for scarce resources such as screen space. In a second step, the cognitive capacity of the driver as another scarce resource has to be considered. For the first step, an algorithm fulfilling the requirements of this situation is presented and evaluated. For the second step, I define the concept of System Situation Awareness (SSA) as an extension of Endsley’s Situation Awareness (SA) model. I claim that not only the driver needs to know what is happening in his environment, but also the system, e.g., the car. In order to achieve SSA, two paths of research have to be followed:
(1) Assessment of cognitive load of the driver in an unobtrusive way. I propose to estimate this value using a model based on environmental data.
(2) Developing model of cognitive complexity induced by messages presented by the system.
Three experiments support the claims I make in my conceptual contribution to this field. A prototypical implementation of the situation-aware presentation management toolkit PRESTK is presented and shown in two demonstrators.In den letzten Jahren hat die Menge der informationsanzeigenden Systeme im Auto drastisch zugenommen. Da sie potenziell unabhängig voneinander ablaufen, erhöhen sie die Gefahr, die Aufmerksamkeit des Fahrers abzulenken. Konflikte entstehen, wenn zwei oder mehr Systeme zeitgleich auf limitierte Ressourcen wie z. B. den Bildschirmplatz zugreifen. Ein erster Schritt, diese Konflikte zu vermeiden, ist die Orchestrierung dieser Systeme mittels Techniken aus dem Bereich Scheduling und Präsentationsplanung. In einem zweiten Schritt sollte die kognitive Kapazität des Fahrers als ebenfalls limitierte Ressource berücksichtigt werden. Der Algorithmus, den ich zu Schritt 1 vorstelle und evaluiere, erfüllt alle diese Anforderungen. Zu Schritt 2 definiere ich das Konzept System Situation Awareness (SSA), basierend auf Endsley’s Konzept der Situation Awareness (SA). Dadurch wird erreicht, dass nicht nur der Fahrer sich seiner Umgebung bewusst ist, sondern auch das System (d.h. das Auto). Zu diesem Zweck m¨ussen zwei Bereiche untersucht werden:
(1) Die kognitive Belastbarkeit des Fahrers unaufdringlich ermitteln. Dazu schlage ich ein Modell vor, das auf Umgebungsinformationen basiert.
(2) Ein weiteres Modell soll die Komplexität der präsentierten Informationen bestimmen. Drei Experimente stützen die Behauptungen in meinem konzeptuellen Beitrag. Ein Prototyp des situationsbewussten Präsentationsmanagement-Toolkits PresTK wird vorgestellt und in zwei Demonstratoren gezeigt