879 research outputs found
Efficient Localization and Tracking of Two Acoustic Sources using Particle Filters with Swarm Intelligence
Publication in the conference proceedings of EUSIPCO, Poznan, Poland, 200
Tracking the Fine Scale Movements of Fish using Autonomous Maritime Robotics: A Systematic State of the Art Review
This paper provides a systematic state of the art review on tracking the fine scale movements of fish with the use of autonomous maritime robotics. Knowledge of migration patterns and the localization of specific species of fish at a given time is vital to many aspects of conservation. This paper reviews these technologies and provides insight into what systems are being used and why. The review results show that a larger amount of complex systems that use a deep learning techniques are used over more simplistic approaches to the design. Most results found in the study involve Autonomous Underwater Vehicles, which generally require the most complex array of sensors. The results also provide insight into future research such as methods involving swarm intelligence, which has seen an increase in use in recent years. This synthesis of current and future research will be helpful to research teams working to create an autonomous vehicle with intentions to track, navigate or survey
Efficient Interferer Cancelation based on Geometrical Information of the Reverberant Environment
reserved7ISSN (online) 2219-5491
5 pagg totaliPagani, P.; Riva, D.; Antonacci, F.; Prandi, G.; Tagliasacchi, M.; Sarti, A.; Tubaro, S.Pagani, P.; Riva, Davide; Antonacci, Fabio; Prandi, Giorgio; Tagliasacchi, Marco; Sarti, Augusto; Tubaro, Stefan
Adaptive and learning-based formation control of swarm robots
Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation
Wireless Sensor Networks for Underwater Localization: A Survey
Autonomous Underwater Vehicles (AUVs) have widely deployed in marine investigation and ocean exploration in recent years. As the fundamental information, their position information is not only for data validity but also for many real-world applications. Therefore, it is critical for the AUV to have the underwater localization capability. This report is mainly devoted to outline the recent advance- ment of Wireless Sensor Networks (WSN) based underwater localization. Several classic architectures designed for Underwater Acoustic Sensor Network (UASN) are brie y introduced. Acoustic propa- gation and channel models are described and several ranging techniques are then explained. Many state-of-the-art underwater localization algorithms are introduced, followed by the outline of some existing underwater localization systems
A survey on gas leakage source detection and boundary tracking with wireless sensor networks
Gas leakage source detection and boundary tracking of continuous objects have received a significant research attention in the academic as well as the industries due to the loss and damage caused by toxic gas leakage in large-scale petrochemical plants. With the advance and rapid adoption of wireless sensor networks (WSNs) in the last decades, source localization and boundary estimation have became the priority of research works. In addition, an accurate boundary estimation is a critical issue due to the fast movement, changing shape, and invisibility of the gas leakage compared with the other single object detections. We present various gas diffusion models used in the literature that offer the effective computational approaches to measure the gas concentrations in the large area. In this paper, we compare the continuous object localization and boundary detection schemes with respect to complexity, energy consumption, and estimation accuracy. Moreover, this paper presents the research directions for existing and future gas leakage source localization and boundary estimation schemes with WSNs
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
A Comprehensive Review on Autonomous Navigation
The field of autonomous mobile robots has undergone dramatic advancements
over the past decades. Despite achieving important milestones, several
challenges are yet to be addressed. Aggregating the achievements of the robotic
community as survey papers is vital to keep the track of current
state-of-the-art and the challenges that must be tackled in the future. This
paper tries to provide a comprehensive review of autonomous mobile robots
covering topics such as sensor types, mobile robot platforms, simulation tools,
path planning and following, sensor fusion methods, obstacle avoidance, and
SLAM. The urge to present a survey paper is twofold. First, autonomous
navigation field evolves fast so writing survey papers regularly is crucial to
keep the research community well-aware of the current status of this field.
Second, deep learning methods have revolutionized many fields including
autonomous navigation. Therefore, it is necessary to give an appropriate
treatment of the role of deep learning in autonomous navigation as well which
is covered in this paper. Future works and research gaps will also be
discussed
Global Localization based on Evolutionary Optimization Algorithms for Indoor and Underground Environments
Mención Internacional en el título de doctorA fully autonomous robot is defined by its capability to sense, understand and move
within the environment to perform a specific task. These qualities are included within
the concept of navigation. However, among them, a basic transcendent one is localization,
the capacity of the system to know its position regarding its surroundings.
Therefore, the localization issue could be defined as searching the robot’s coordinates
and rotation angles within a known environment. In this thesis, the particular case
of Global Localization is addressed, when no information about the initial position
is known, and the robot relies only on its sensors. This work aims to develop several
tools that allow the system to locate in the two most usual geometric map representations:
occupancy maps and Point Clouds. The former divides the dimensional
space into equally-sized cells coded with a binary value distinguishing between free
and occupied space. Point Clouds define obstacles and environment features as a
sparse set of points in the space, commonly measured through a laser sensor.
In this work, various algorithms are presented to search for that position through
laser measurements only, in contrast with more usual methods that combine external
information with motion information of the robot, odometry. Therefore, the system
is capable of finding its own position in indoor environments, with no necessity of
external positioning and without the influence of the uncertainty that motion sensors
typically induce. Our solution is addressed by implementing various stochastic optimization
algorithms or Meta-heuristics, specifically those bio-inspired or commonly
known as Evolutionary Algorithms. Inspired by natural phenomena, these algorithms
are based on the evolution of a series of particles or population members towards a
solution through the optimization of a cost or fitness function that defines the problem.
The implemented algorithms are Differential Evolution, Particle Swarm Optimization,
and Invasive Weed Optimization, which try to mimic the behavior of evolution
through mutation, the movement of swarms or flocks of animals, and the colonizing
behavior of invasive species of plants respectively. The different implementations
address the necessity to parameterize these algorithms for a wide search space as
a complete three-dimensional map, with exploratory behavior and the convergence
conditions that terminate the search. The process is a recursive optimum estimation search, so the solution is unknown. These implementations address the optimum
localization search procedure by comparing the laser measurements from the real position
with the one obtained from each candidate particle in the known map. The
cost function evaluates this similarity between real and estimated measurements and,
therefore, is the function that defines the problem to optimize.
The common approach in localization or mapping using laser sensors is to establish
the mean square error or the absolute error between laser measurements as an
optimization function. In this work, a different perspective is introduced by benefiting
from statistical distance or divergences, utilized to describe the similarity between
probability distributions. By modeling the laser sensor as a probability distribution
over the measured distance, the algorithm can benefit from the asymmetries provided
by these divergences to favor or penalize different situations. Hence, how the laser
scans differ and not only how much can be evaluated. The results obtained in different
maps, simulated and real, prove that the Global Localization issue is successfully
solved through these methods, both in position and orientation. The implementation
of divergence-based weighted cost functions provides great robustness and accuracy
to the localization filters and optimal response before different sources and noise levels
from sensor measurements, the environment, or the presence of obstacles that are not
registered in the map.Lo que define a un robot completamente autónomo es su capacidad para percibir el entorno,
comprenderlo y poder desplazarse en ´el para realizar las tareas encomendadas.
Estas cualidades se engloban dentro del concepto de la navegación, pero entre todas
ellas la más básica y de la que dependen en buena parte el resto es la localización,
la capacidad del sistema de conocer su posición respecto al entorno que lo rodea. De
esta forma el problema de la localización se podría definir como la búsqueda de las
coordenadas de posición y los ángulos de orientación de un robot móvil dentro de un
entorno conocido. En esta tesis se aborda el caso particular de la localización global,
cuando no existe información inicial alguna y el sistema depende únicamente de sus
sensores. El objetivo de este trabajo es el desarrollo de varias herramientas que permitan
que el sistema encuentre la localización en la que se encuentra respecto a los
dos tipos de mapa más comúnmente utilizados para representar el entorno: los mapas
de ocupación y las nubes de puntos. Los primeros subdividen el espacio en celdas
de igual tamaño cuyo valor se define de forma binaria entre espacio libre y ocupado.
Las nubes de puntos definen los obstáculos como una serie dispersa de puntos en el
espacio comúnmente medidos a través de un láser.
En este trabajo se presentan varios algoritmos para la búsqueda de esa posición utilizando únicamente las medidas de este sensor láser, en contraste con los métodos más
habituales que combinan información externa con información propia del movimiento
del robot, la odometría. De esta forma el sistema es capaz de encontrar su posición
en entornos interiores sin depender de posicionamiento externo y sin verse influenciado
por la deriva típica que inducen los sensores de movimiento. La solución se
afronta mediante la implementación de varios tipos de algoritmos estocásticos de optimización o Meta-heurísticas, en concreto entre los denominados bio-inspirados o
comúnmente conocidos como Algoritmos Evolutivos. Estos algoritmos, inspirados en
varios fenómenos de la naturaleza, se basan en la evolución de una serie de partículas
o población hacia una solución en base a la optimización de una función de coste que
define el problema.
Los algoritmos implementados en este trabajo son Differential Evolution, Particle
Swarm Optimization e Invasive Weed Optimization, que tratan de imitar el comportamiento
de la evolución por mutación, el movimiento de enjambres o bandas de animales y la colonización por parte de especies invasivas de plantas respectivamente.
Las distintas implementaciones abordan la necesidad de parametrizar estos algoritmos
para un espacio de búsqueda muy amplio como es un mapa completo, con la
necesidad de que su comportamiento sea muy exploratorio, así como las condiciones
de convergencia que definen el fin de la búsqueda ya que al ser un proceso recursivo
de estimación la solución no es conocida. Estos algoritmos plantean la forma de
buscar la localización ´optima del robot mediante la comparación de las medidas del
láser en la posición real con lo esperado en la posición de cada una de esas partículas
teniendo en cuenta el mapa conocido. La función de coste evalúa esa semejanza entre
las medidas reales y estimadas y por tanto, es la función que define el problema.
Las funciones típicamente utilizadas tanto en mapeado como localización mediante
el uso de sensores láser de distancia son el error cuadrático medio o el error
absoluto entre distancia estimada y real. En este trabajo se presenta una perspectiva
diferente, aprovechando las distancias estadísticas o divergencias, utilizadas para
establecer la semejanza entre distribuciones probabilísticas. Modelando el sensor
como una distribución de probabilidad entorno a la medida aportada por el láser, se
puede aprovechar la asimetría de esas divergencias para favorecer o penalizar distintas
situaciones. De esta forma se evalúa como difieren las medias y no solo cuanto. Los
resultados obtenidos en distintos mapas tanto simulados como reales demuestran que
el problema de la localización se resuelve con éxito mediante estos métodos tanto respecto
al error de estimación de la posición como de la orientación del robot. El uso de
las divergencias y su implementación en una función de coste ponderada proporciona
gran robustez y precisión al filtro de localización y gran respuesta ante diferentes
fuentes y niveles de ruido, tanto de la propia medida del sensor, del ambiente y de
obstáculos no modelados en el mapa del entorno.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Fabio Bonsignorio.- Secretario: María Dolores Blanco Rojas.- Vocal: Alberto Brunete Gonzále
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