83 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
Performance Evaluation of Source Routing Minimum Cost Forwarding Protocol over 6TiSCH Applied to the OpenMote-B Platform
The aim of this work is the development of Source Routing Minimum Cost Forwarding (SRMCF) protocol over IPv6 over the TSCH mode of IEEE 802.15.4e (6TiSCH), evaluating the performance of these protocols for the Internet of Things (IoT). To perform this evaluation, this work is making use of the OpenWSN project platform, which implements IEEE 802.15.4e in an open source environment. The evaluation process is also being done in the most recent version of the OpenMote-B platform. Another goal of this collaboration is to give contribution to the investigation of the applicability of quality of service (QoS) applied to the IEEE 802.15.4e standard. In the present stage of development, the efforts are concentrated on the programming of the required code, and the adaptation of the OpenWSN stack. Once the programming code is implemented, the team will investigate the possibilities to apply quality of service over the stack developed. Next, the team will also investigate the possibilities to explore long range routing techniques using the OpenMote platforms. In this task, we will use xBee, LoraWAN, Raspberry PI and Arduino platforms.info:eu-repo/semantics/publishedVersio
Drone Base Station Trajectory Management for Optimal Scheduling in LTE-Based Sparse Delay-Sensitive M2M Networks
Providing connectivity in areas out of reach of the cellular infrastructure is a very active area of research. This connectivity is particularly needed in case of the deployment of machine type communication devices (MTCDs) for critical purposes such as homeland security. In such applications, MTCDs are deployed in areas that are hard to reach using regular communications infrastructure while the collected data is timely critical. Drone-supported communications constitute a new trend in complementing the reach of the terrestrial communication infrastructure. In this study, drones are used as base stations to provide real-time communication services to gather critical data out of a group of MTCDs that are sparsely deployed in a marine environment. Studying different communication technologies as LTE, WiFi, LPWAN and Free-Space Optical communication (FSOC) incorporated with the drone communications was important in the first phase of this research to identify the best candidate for addressing this need. We have determined the cellular technology, and particularly LTE, to be the most suitable candidate to support such applications. In this case, an LTE base station would be mounted on the drone which will help communicate with the different MTCDs to transmit their data to the network backhaul. We then formulate the problem model mathematically and devise the trajectory planning and scheduling algorithm that decides the drone path and the resulting scheduling. Based on this formulation, we decided to compare between an Ant Colony Optimization (ACO) based technique that optimizes the drone movement among the sparsely-deployed MTCDs and a Genetic Algorithm (GA) based solution that achieves the same purpose. This optimization is based on minimizing the energy cost of the drone movement while ensuring the data transmission deadline missing is minimized. We present the results of several simulation experiments that validate the different performance aspects of the technique
IoT enabled aquatic drone for environment monitoring
This thesis presents a platform that tackles environment monitoring by using air and water
quality sensors to provide data for the user to know what is happening in that surveilled area.
The hardware is incorporated in a sensing module in order to be used with an Unmanned
Surface Vehicle (USV).
It presents a monitoring system based on Raspberry Pi platform and a multichannel sensing
module associated with water quality and air quality measurement parameters. Thus, the
temperature, relative humidity and gas concentration are measured as well as the underwater
acoustic signals using a hydrophone. The data is stored on the memory of the drone’s
computational platform (Raspberry Pi), and synchronized with a remote server database. Audio
streaming capabilities were implemented in the server side. Additionally, a mobile application
was developed to be used by people working in the field for data visualization, audio streaming
playback and statistical analysis (by showing plotted data).O intuito desta dissertação é apresentar uma plataforma de monitorização ambiental através da
instalação de sensores de qualidade do ar e da água de forma a fornecer dados ao utilizador
daquela área vigiada. O hardware é apresentado num módulo onde estão presentes todos os
componentes por forma a poder ser usado num drone aquático.
É apresentado um sistema de monitorização baseado no sistema de processamento Raspberry
Pi e um módulo multicanal de sensores de medição de qualidade do ar e qualidade da água.
Sensores esses de medição da temperatura, humidade relativa e concentrações de gases tal como
a medição de sinais de áudio debaixo de água com o uso de um hidrofone. Os dados estão
alojados na memória do sistema computacional do drone (Raspberry Pi) e estão sincronizados
com uma base de dados remota alojada num servidor cloud. Um sistema de streaming de áudio
foi também implementado do lado do servidor. Adicionalmente, foi desenvolvida uma
aplicação móvel que permite visualizar os dados provenientes dos sensores, reproduzir a stream
de áudio e também analise de estatísticas (com apresentação gráfica dos dados)
AUTONOMOUS SYSTEMS & SAFETY ISSUES: THE ROADMAP TO ENABLE NEW ADVANCES IN INDUSTRIAL APPLICATIONS
The paper addresses the safety issues related to the development of new solutions based on autonomous systems for industrial applications and the necessity to develop experimental environments for investigating these cases; a set of examples is proposed in order to provide cases and challenges as well as to suggest approaches to address these problems
Forests
In this paper, we provide an overview of positioning systems for moving resources in forest and fire management and review the related literature. Emphasis is placed on the accuracy and range of different localization and location-sharing methods, particularly in forested environments and in the absence of conventional cellular or internet connectivity. We then conduct a second review of literature and concepts related to several emerging, broad themes in data science, including the terms |, |, |, |, |, |, and |. Our objective in this second review is to inform how these broader concepts, with implications for networking and analytics, may help to advance natural resource management and science in the future. Based on methods, themes, and concepts that arose in our systematic reviews, we then augmented the paper with additional literature from wildlife and fisheries management, as well as concepts from video object detection, relative positioning, and inventory-tracking that are also used as forms of localization. Based on our reviews of positioning technologies and emerging data science themes, we present a hierarchical model for collecting and sharing data in forest and fire management, and more broadly in the field of natural resources. The model reflects tradeoffs in range and bandwidth when recording, processing, and communicating large quantities of data in time and space to support resource management, science, and public safety in remote areas. In the hierarchical approach, wearable devices and other sensors typically transmit data at short distances using Bluetooth, Bluetooth Low Energy (BLE), or ANT wireless, and smartphones and tablets serve as intermediate data collection and processing hubs for information that can be subsequently transmitted using radio networking systems or satellite communication. Data with greater spatial and temporal complexity is typically processed incrementally at lower tiers, then fused and summarized at higher levels of incident command or resource management. Lastly, we outline several priority areas for future research to advance big data analytics in natural resources.U01 OH010841/OH/NIOSH CDC HHSUnited States/U54 OH007544/OH/NIOSH CDC HHSUnited States
Optimization and Communication in UAV Networks
UAVs are becoming a reality and attract increasing attention. They can be remotely controlled or completely autonomous and be used alone or as a fleet and in a large set of applications. They are constrained by hardware since they cannot be too heavy and rely on batteries. Their use still raises a large set of exciting new challenges in terms of trajectory optimization and positioning when they are used alone or in cooperation, and communication when they evolve in swarm, to name but a few examples. This book presents some new original contributions regarding UAV or UAV swarm optimization and communication aspects
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
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