3,584 research outputs found
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Coverage Path Planning for Autonomous Robots
Coverage Path Planning (CPP) is a problem of path computation with minimal length that guarantees to scan the entire area of interest. CPP finds its application in diverse fields like cartography, inspection, precision agriculture, milling, and demining. However, this thesis is a prominent step to solve CPP for real-world problems where environment poses multiple challenges. At first, four significant and pressing challenges for CPP in extreme environment are identified. Each challenge is formulated as a problem and its solution has been presented as a dedicated chapter in this thesis. The first problem, Goal-Oriented Sensor based CPP, focuses on cumbersome tasks like Nuclear Decommissioning, where the robot covers an abandoned site in tandem with the goal to reach a static target in minimal time. To meet the grave speeding-up challenge, a novel offline-online strategy is proposed that efficiently models the site using floor plans and grid maps as a priori information. The proposed strategy outperforms the two baseline approaches with reduction in coverage time by 45%- 82%. The second problem explores CPP of distributed regions, applicable in post-disaster scenarios like Fukushima Daiichi.
Experiments are conducted at radiation laboratory to identify the constraints robot would be subjected to. The thesis is successfully able to diagnose transient damage in the robot’s sensor after 3 Gy of gamma radiation exposure. Therefore, a region order travel constraint known as Precedence Provision is imposed for successful coverage. The region order constraint allows the coverage length to be minimised by 65% in comparison to state-of-the-art techniques. The third problem identifies the major bottleneck of limited on-board energy that inhibits complete coverage of distributed regions. The existing approaches allow robots to undertake multiple tours for complete coverage which is impractical in many scenarios. To this end, a novel algorithm is proposed that solves a variant of CPP where the robot aims to achieve near-optimal area coverage due to path length limitation caused by the energy constraint. The proposed algorithm covers 23% - 35% more area in comparison to the state-of-the-art approaches. Finally, the last problem, an extension of the second and third problems, deals with the problem of CPP over a set of disjoint regions using a fleet of heterogeneous aerial robots. A heuristic is proposed to deliver solutions within acceptable time limits. The experiments demonstrate that the proposed heuristic solution reduces the energy cost by 15-40% in comparison to the state-of-the art solutions
Routing optimization algorithms in integrated fronthaul/backhaul networks supporting multitenancy
Mención Internacional en el tÃtulo de doctorEsta tesis pretende ayudar en la definición y el diseño de la quinta generación de
redes de telecomunicaciones (5G) a través del modelado matemático de las diferentes
cualidades que las caracterizan. En general, la ambición de estos modelos es realizar
una optimización de las redes, ensalzando sus capacidades recientemente adquiridas para
mejorar la eficiencia de los futuros despliegues tanto para los usuarios como para los
operadores. El periodo de realización de esta tesis se corresponde con el periodo de
investigación y definición de las redes 5G, y, por lo tanto, en paralelo y en el contexto
de varios proyectos europeos del programa H2020. Por lo tanto, las diferentes partes
del trabajo presentado en este documento cuadran y ofrecen una solución a diferentes
retos que han ido apareciendo durante la definición del 5G y dentro del ámbito de estos
proyectos, considerando los comentarios y problemas desde el punto de vista de todos los
usuarios finales, operadores y proveedores.
AsÃ, el primer reto a considerar se centra en el núcleo de la red, en particular en
cómo integrar tráfico fronthaul y backhaul en el mismo estrato de transporte. La solución
propuesta es un marco de optimización para el enrutado y la colocación de recursos que
ha sido desarrollado teniendo en cuenta restricciones de retardo, capacidad y caminos,
maximizando el grado de despliegue de Unidades Distribuidas (DU) mientras se minimizan
los agregados de las Unidades Centrales (CU) que las soportan. El marco y los algoritmos
heurÃsticos desarrollados (para reducir la complexidad computacional) son validados y
aplicados a redes tanto a pequeña como a gran (nivel de producción) escala. Esto los
hace útiles para los operadores de redes tanto para la planificación de la red como para
el ajuste dinámico de las operaciones de red en su infraestructura (virtualizada).
Moviéndonos más cerca de los usuarios, el segundo reto considerado se centra en
la colocación de servicios en entornos de nube y borde (cloud/edge). En particular, el
problema considerado consiste en seleccionar la mejor localización para cada función
de red virtual (VNF) que compone un servicio en entornos de robots en la nube, que
implica restricciones estrictas en las cotas de retardo y fiabilidad. Los robots, vehÃculos y
otros dispositivos finales proveen competencias significativas como impulsores, sensores y
computación local que son esenciales para algunos servicios. Por contra, estos dispositivos
están en continuo movimiento y pueden perder la conexión con la red o quedarse sin baterÃa, cosa que reta aún más la entrega de servicios en este entorno dinámico. AsÃ, el
análisis realizado y la solución propuesta abordan las restricciones de movilidad y baterÃa.
Además, también se necesita tener en cuenta los aspectos temporales y los objetivos
conflictivos de fiabilidad y baja latencia en el despliegue de servicios en una red volátil,
donde los nodos de cómputo móviles actúan como una extensión de la infraestructura
de cómputo de la nube y el borde. El problema se formula como un problema de
optimización para colocación de VNFs minimizando el coste y también se propone un
heurÃstico eficiente. Los algoritmos son evaluados de forma extensiva desde varios aspectos
por simulación en escenarios que reflejan la realidad de forma detallada.
Finalmente, el último reto analizado se centra en dar soporte a servicios basados en
el borde, en particular, aprendizaje automático (ML) en escenarios del Internet de las
Cosas (IoT) distribuidos. El enfoque tradicional al ML distribuido se centra en adaptar
los algoritmos de aprendizaje a la red, por ejemplo, reduciendo las actualizaciones para
frenar la sobrecarga. Las redes basadas en el borde inteligente, en cambio, hacen posible
seguir un enfoque opuesto, es decir, definir la topologÃa de red lógica alrededor de la
tarea de aprendizaje a realizar, para asà alcanzar el resultado de aprendizaje deseado.
La solución propuesta incluye un modelo de sistema que captura dichos aspectos en
el contexto de ML supervisado, teniendo en cuenta tanto nodos de aprendizaje (que
realizan las computaciones) como nodos de información (que proveen datos). El problema
se formula para seleccionar (i) qué nodos de aprendizaje e información deben cooperar
para completar la tarea de aprendizaje, y (ii) el número de iteraciones a realizar, para
minimizar el coste de aprendizaje mientras se garantizan los objetivos de error predictivo y
tiempo de ejecución. La solución también incluye un algoritmo heurÃstico que es evaluado
ensalzando una topologÃa de red real y considerando tanto las tareas de clasificación
como de regresión, y cuya solución se acerca mucho al óptimo, superando las soluciones
alternativas encontradas en la literatura.This thesis aims to help in the definition and design of the 5th generation of
telecommunications networks (5G) by modelling the different features that characterize
them through several mathematical models. Overall, the aim of these models is to perform
a wide optimization of the network elements, leveraging their newly-acquired capabilities
in order to improve the efficiency of the future deployments both for the users and the
operators. The timeline of this thesis corresponds to the timeline of the research and
definition of 5G networks, and thus in parallel and in the context of several European
H2020 programs. Hence, the different parts of the work presented in this document
match and provide a solution to different challenges that have been appearing during
the definition of 5G and within the scope of those projects, considering the feedback and
problems from the point of view of all the end users, operators and providers.
Thus, the first challenge to be considered focuses on the core network, in particular
on how to integrate fronthaul and backhaul traffic over the same transport stratum.
The solution proposed is an optimization framework for routing and resource placement
that has been developed taking into account delay, capacity and path constraints,
maximizing the degree of Distributed Unit (DU) deployment while minimizing the
supporting Central Unit (CU) pools. The framework and the developed heuristics (to
reduce the computational complexity) are validated and applied to both small and largescale
(production-level) networks. They can be useful to network operators for both
network planning as well as network operation adjusting their (virtualized) infrastructure
dynamically.
Moving closer to the user side, the second challenge considered focuses on the
allocation of services in cloud/edge environments. In particular, the problem tackled
consists of selecting the best the location of each Virtual Network Function (VNF)
that compose a service in cloud robotics environments, that imply strict delay bounds
and reliability constraints. Robots, vehicles and other end-devices provide significant
capabilities such as actuators, sensors and local computation which are essential for some
services. On the negative side, these devices are continuously on the move and might
lose network connection or run out of battery, which further challenge service delivery in
this dynamic environment. Thus, the performed analysis and proposed solution tackle the mobility and battery restrictions. We further need to account for the temporal aspects and
conflicting goals of reliable, low latency service deployment over a volatile network, where
mobile compute nodes act as an extension of the cloud and edge computing infrastructure.
The problem is formulated as a cost-minimizing VNF placement optimization and an
efficient heuristic is proposed. The algorithms are extensively evaluated from various
aspects by simulation on detailed real-world scenarios.
Finally, the last challenge analyzed focuses on supporting edge-based services, in
particular, Machine Learning (ML) in distributed Internet of Things (IoT) scenarios. The
traditional approach to distributed ML is to adapt learning algorithms to the network, e.g.,
reducing updates to curb overhead. Networks based on intelligent edge, instead, make
it possible to follow the opposite approach, i.e., to define the logical network topology
around the learning task to perform, so as to meet the desired learning performance.
The proposed solution includes a system model that captures such aspects in the context
of supervised ML, accounting for both learning nodes (that perform computations) and
information nodes (that provide data). The problem is formulated to select (i) which
learning and information nodes should cooperate to complete the learning task, and (ii)
the number of iterations to perform, in order to minimize the learning cost while meeting
the target prediction error and execution time. The solution also includes an heuristic
algorithm that is evaluated leveraging a real-world network topology and considering
both classification and regression tasks, and closely matches the optimum, outperforming
state-of-the-art alternatives.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en IngenierÃa Telemática por la Universidad Carlos III de MadridPresidente: Pablo Serrano Yáñez-Mingot.- Secretario: Andrés GarcÃa Saavedra.- Vocal: Luca Valcarengh
Simulation in Automated Guided Vehicle System Design
The intense global competition that manufacturing companies face today results in an
increase of product variety and shorter product life cycles. One response to this threat is
agile manufacturing concepts. This requires materials handling systems that are agile
and capable of reconfiguration. As competition in the world marketplace becomes
increasingly customer-driven, manufacturing environments must be highly
reconfigurable and responsive to accommodate product and process changes, with rigid,
static automation systems giving way to more flexible types.
Automated Guided Vehicle Systems (AGVS) have such capabilities and AGV
functionality has been developed to improve flexibility and diminish the traditional
disadvantages of AGV-systems. The AGV-system design is however a multi-faceted
problem with a large number of design factors of which many are correlating and
interdependent. Available methods and techniques exhibit problems in supporting the
whole design process. A research review of the work reported on AGVS development in
combination with simulation revealed that of 39 papers only four were industrially
related. Most work was on the conceptual design phase, but little has been reported on
the detailed simulation of AGVS.
Semi-autonomous vehicles (SA V) are an innovative concept to overcome the problems
of inflexible -systems and to improve materials handling functionality. The SA V
concept introduces a higher degree of autonomy in industrial AGV -systems with the
man-in-the-Ioop. The introduction of autonomy in industrial applications is approached
by explicitly controlling the level of autonomy at different occasions. The SA V s are
easy to program and easily reconfigurable regarding navigation systems and material
handling equipment. Novel approaches to materials handling like the SA V -concept
place new requirements on the AGVS development and the use of simulation as a part
of the process. Traditional AGV -system simulation approaches do not fully meet these
requirements and the improved functionality of AGVs is not used to its full power.
There is a considerflble potential in shortening the AGV -system design-cycle, and thus
the manufacturing system design-cycle, and still achieve more accurate solutions well
suited for MRS tasks.
Recent developments in simulation tools for manufacturing have improved production
engineering development and the tools are being adopted more widely in industry. For
the development of AGV -systems this has not fully been exploited. Previous research
has focused on the conceptual part of the design process and many simulation
approaches to AGV -system design lack in validity. In this thesis a methodology is
proposed for the structured development of AGV -systems using simulation. Elements of
this methodology address the development of novel functionality.
The objective of the first research case of this research study was to identify factors for
industrial AGV -system simulation. The second research case focuses on simulation in
the design of Semi-autonomous vehicles, and the third case evaluates a simulation based
design framework. This research study has advanced development by offering a
framework for developing testing and evaluating AGV -systems, based on concurrent
development using a virtual environment. The ability to exploit unique or novel features
of AGVs based on a virtual environment improves the potential of AGV-systems
considerably.University of Skovde. European Commission for funding the INCO/COPERNICUS Projec
Cloud-Enhanced Robotic System for Smart City Crowd Control
Cloud robotics in smart cities is an emerging paradigm that enables autonomous robotic agents to communicate and collaborate with a cloud computing infrastructure. It complements the Internet of Things (IoT) by creating an expanded network where robots offload data-intensive computation to the ubiquitous cloud to ensure quality of service (QoS). However, offloading for robots is significantly complex due to their unique characteristics of mobility, skill-learning, data collection, and decision-making capabilities. In this paper, a generic cloud robotics framework is proposed to realize smart city vision while taking into consideration its various complexities. Specifically, we present an integrated framework for a crowd control system where cloud-enhanced robots are deployed to perform necessary tasks. The task offloading is formulated as a constrained optimization problem capable of handling any task flow that can be characterized by a Direct Acyclic Graph (DAG).We consider two scenarios of minimizing energy and time, respectively, and develop a genetic algorithm (GA)-based approach to identify the optimal task offloading decisions. The performance comparison with two benchmarks shows that our GA scheme achieves desired energy and time performance. We also show the adaptability of our algorithm by varying the values for bandwidth and movement. The results suggest their impact on offloading. Finally, we present a multi-task flow optimal path sequence problem that highlights how the robot can plan its task completion via movements that expend the minimum energy. This integrates path planning with offloading for robotics. To the best of our knowledge, this is the first attempt to evaluate cloud-based task offloading for a smart city crowd control system
Coverage & cooperation: Completing complex tasks as quickly as possible using teams of robots
As the robotics industry grows and robots enter our homes and public spaces, they are increasingly expected to work in cooperation with each other. My thesis focuses on multirobot planning, specifically in the context of coverage robots, such as robotic lawnmowers and vacuum cleaners.
Two problems unique to multirobot teams are task allocation and search. I present a task allocation algorithm which balances the workload amongst all robots in the team with the objective of minimizing the overall mission time. I also present a search algorithm which robots can use to find lost teammates. It uses a probabilistic belief of a target robot’s position to create a planning tree and then searches by following the best path in the tree.
For robust multirobot coverage, I use both the task allocation and search algorithms. First the coverage region is divided into a set of small coverage tasks which minimize the number of turns the robots will need to take. These tasks are then allocated to individual robots. During the mission, robots replan with nearby robots to rebalance the workload and, once a robot has finished its tasks, it searches for teammates to help them finish their tasks faster
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