668 research outputs found
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
Noise, Bifurcations, and Modeling of Interacting Particle Systems
We consider the stochastic patterns of a system of communicating, or coupled,
self-propelled particles in the presence of noise and communication time delay.
For sufficiently large environmental noise, there exists a transition between a
translating state and a rotating state with stationary center of mass. Time
delayed communication creates a bifurcation pattern dependent on the coupling
amplitude between particles. Using a mean field model in the large number
limit, we show how the complete bifurcation unfolds in the presence of
communication delay and coupling amplitude. Relative to the center of mass, the
patterns can then be described as transitions between translation, rotation
about a stationary point, or a rotating swarm, where the center of mass
undergoes a Hopf bifurcation from steady state to a limit cycle. Examples of
some of the stochastic patterns will be given for large numbers of particles
Evolution of collective behaviors for a real swarm of aquatic surface robots
Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.info:eu-repo/semantics/publishedVersio
Constructing living buildings: a review of relevant technologies for a novel application of biohybrid robotics
Biohybrid robotics takes an engineering approach to the expansion and exploitation of biological behaviours for application to automated tasks. Here, we identify the construction of living buildings and infrastructure as a high-potential application domain for biohybrid robotics, and review technological advances relevant to its future development. Construction, civil infrastructure maintenance and building occupancy in the last decades have comprised a major portion of economic production, energy consumption and carbon emissions. Integrating biological organisms into automated construction tasks and permanent building components therefore has high potential for impact. Live materials can provide several advantages over standard synthetic construction materials, including self-repair of damage, increase rather than degradation of structural performance over time, resilience to corrosive environments, support of biodiversity, and mitigation of urban heat islands. Here, we review relevant technologies, which are currently disparate. They span robotics, self-organizing systems, artificial life, construction automation, structural engineering, architecture, bioengineering, biomaterials, and molecular and cellular biology. In these disciplines, developments relevant to biohybrid construction and living buildings are in the early stages, and typically are not exchanged between disciplines. We, therefore, consider this review useful to the future development of biohybrid engineering for this highly interdisciplinary application.publishe
Kollektiivisen liikkeen ohjaaminen
Collective motion is an umbrella term for both biological and non-organic coherent motion, in which tens to tens of millions units take part in. The huge fish schools that fishing ships pursue and the nightmarish legions of locusts which destroy entire countries' worth of crops are just a few examples of collective motion in nature that have a direct effect on us humans.
This thesis focuses on the complex behavior of collective motion and studies how such movement can be steered. As a tool, the original Vicsek model for simulating collective behavior is used. An agent-based model, the Vicsek model was introduced in 1995 and has been extensively utilized and studied since. The Vicsek model consists of units that move independently but prefer to take the common movement direction of their neighbors. Although it is a simplified model, the Vicsek model exhibits flocking behavior that is similar to what is observed in nature.
The results of this thesis show that in this context, collective motion of hundreds of units is greatly affected by just a small percentage of special units, called leaders. The leaders don't adhere to the common rules of the other units, but move in a constant direction. It is observed that the relative amount of leaders needed to steer the entire flock actually decreases as the flock size grows or if we wait sufficiently long. This leads to the conclusion that in the limit of an infinite system size, a finite amount of leaders would suffice to control the flock.Kollektiivinen liike on kattotermi sekä biologiselle että ei-orgaaniselle koherentille liikkeelle, johon osallistuu kymmenistä kymmeniin miljooniin yksikköä. Luonnossa esiintyvä kollektiivinen liike vaikuttaa suoraan meihin ihmisiinkin, kuten esimerkiksi kalastuslaivastojen metsästämät kalaparvet tai valtavat kulkusirkkaparvet jotka tuhoavat kokonaisten valtioiden viljasatoja osoittavat.
Tämä diplomityö keskittyy kollektiivisen liikkeen kompleksiseen käytökseen sekä tutkii kuinka tällaista liikettä voidaan ohjata. Työkaluna käytetään kollektiivisen käytöksen simuloimiseen tarkoitettua Vicsekin alkuperäismallia. Vicsekin malli on agenttipohjainen malli joka esiteltiin vuonna 1995, ja jota on siitä lähtien käytetty ja tutkittu laajasti. Vicsekin malli koostuu yksiköistä jotka liikkuvat itsenäisesti, mutta suosivat läheisten yksiköiden keskimääräistä liikesuuntaa. Vaikkakin Vicsekin malli on yksinkertaistettu, sen tuottama parvikäytös vastaa luonnossa havaittavaa käytöstä.
Tämän diplomityön tulokset osoittavat satojen yksikköjen kollektiivisen liikkeen käytöksen olevan riippuvainen vain pienen prosenttiosuuden muodostavien erityisten johtoyksiköiden käytöksestä. Johtoyksiköt eivät noudata samoja sääntöjä kuin muut yksiköt, vaan liikkuvat vakiosuuntaan. Kun parven koko kasvaa tai odotettaessa riittävän kauan, koko parvea ohjaamaan tarvittavien johtoyksiköiden suhteellinen lukumäärää vähenee. Tästä voidaan päätellä että äärettömän kokoisessa systeemissä äärellinen määrä johtoyksiköitä riittää kontrolloimaan koko parvea
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
Recent Advances in Multi Robot Systems
To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems
Coherent Pattern Prediction in Swarms of Delay-Coupled Agents
We consider a general swarm model of self-propelling agents interacting
through a pairwise potential in the presence of noise and communication time
delay. Previous work [Phys. Rev. E 77, 035203(R) (2008)] has shown that a
communication time delay in the swarm induces a pattern bifurcation that
depends on the size of the coupling amplitude. We extend these results by
completely unfolding the bifurcation structure of the mean field approximation.
Our analysis reveals a direct correspondence between the different dynamical
behaviors found in different regions of the coupling-time delay plane with the
different classes of simulated coherent swarm patterns. We derive the
spatio-temporal scales of the swarm structures, and also demonstrate how the
complicated interplay of coupling strength, time delay, noise intensity, and
choice of initial conditions can affect the swarm. In particular, our studies
show that for sufficiently large values of the coupling strength and/or the
time delay, there is a noise intensity threshold that forces a transition of
the swarm from a misaligned state into an aligned state. We show that this
alignment transition exhibits hysteresis when the noise intensity is taken to
be time dependent
Flocking algorithm for autonomous flying robots
Animal swarms displaying a variety of typical flocking patterns would not
exist without underlying safe, optimal and stable dynamics of the individuals.
The emergence of these universal patterns can be efficiently reconstructed with
agent-based models. If we want to reproduce these patterns with artificial
systems, such as autonomous aerial robots, agent-based models can also be used
in the control algorithm of the robots. However, finding the proper algorithms
and thus understanding the essential characteristics of the emergent collective
behaviour of robots requires the thorough and realistic modeling of the robot
and the environment as well. In this paper, first, we present an abstract
mathematical model of an autonomous flying robot. The model takes into account
several realistic features, such as time delay and locality of the
communication, inaccuracy of the on-board sensors and inertial effects. We
present two decentralized control algorithms. One is based on a simple
self-propelled flocking model of animal collective motion, the other is a
collective target tracking algorithm. Both algorithms contain a viscous
friction-like term, which aligns the velocities of neighbouring agents parallel
to each other. We show that this term can be essential for reducing the
inherent instabilities of such a noisy and delayed realistic system. We discuss
simulation results about the stability of the control algorithms, and perform
real experiments to show the applicability of the algorithms on a group of
autonomous quadcopters
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