664 research outputs found
Enabling Cyber Physical Systems with Wireless Sensor Networking Technologies
[[abstract]]Over the last few years, we have witnessed a growing interest in Cyber Physical Systems (CPSs) that rely on a strong synergy between computational and physical components. CPSs are expected to have a tremendous impact on many critical sectors (such as energy, manufacturing, healthcare, transportation, aerospace, etc) of the economy. CPSs have the ability to transform the way human-to-human, human-toobject, and object-to-object interactions take place in the physical and virtual worlds. The increasing pervasiveness of Wireless Sensor Networking (WSN) technologies in many applications make them an important component of emerging CPS designs. We present some of the most important design requirements of CPS architectures. We discuss key sensor network characteristics that can be leveraged in CPS designs. In addition, we also review a few well-known CPS application domains that depend on WSNs in their design architectures and implementations. Finally, we present some of the challenges that still need to be addressed to enable seamless integration of WSN with CPS designs.[[incitationindex]]SCI[[booktype]]ç´™
Behavior-based Control for Service Robots inspired by Human Motion Patterns : a Robotic Shopping Assistant
Es wurde, unter Verwendung menschenähnlicher Bewegungsmuster und eines verhaltensbasierten Ansatzes, eine Steuerung für mobile Serviceroboter entwickelt, die Aufgabenplanung, globale und lokale Navigation in dynamischen Umgebungen, sowie die gemeinsame Aufgabenausführung mit einem Benutzer umfasst. Das Verhaltensnetzwerk besteht aus Modulen mit voneinander unabhängigen Aufgaben. Das komplexe Gesamtverhalten des Systems ergibt sich durch die Vereinigung der Einzelverhalten (\u27Emergenz\u27)
Zero-Shot Multi-View Indoor Localization via Graph Location Networks
Indoor localization is a fundamental problem in location-based applications.
Current approaches to this problem typically rely on Radio Frequency
technology, which requires not only supporting infrastructures but human
efforts to measure and calibrate the signal. Moreover, data collection for all
locations is indispensable in existing methods, which in turn hinders their
large-scale deployment. In this paper, we propose a novel neural network based
architecture Graph Location Networks (GLN) to perform infrastructure-free,
multi-view image based indoor localization. GLN makes location predictions
based on robust location representations extracted from images through
message-passing networks. Furthermore, we introduce a novel zero-shot indoor
localization setting and tackle it by extending the proposed GLN to a dedicated
zero-shot version, which exploits a novel mechanism Map2Vec to train
location-aware embeddings and make predictions on novel unseen locations. Our
extensive experiments show that the proposed approach outperforms
state-of-the-art methods in the standard setting, and achieves promising
accuracy even in the zero-shot setting where data for half of the locations are
not available. The source code and datasets are publicly available at
https://github.com/coldmanck/zero-shot-indoor-localization-release.Comment: Accepted at ACM MM 2020. 10 pages, 7 figures. Code and datasets
available at
https://github.com/coldmanck/zero-shot-indoor-localization-releas
A COGNITIVE ARCHITECTURE FOR AMBIENT INTELLIGENCE
L’Ambient Intelligence (AmI) è caratterizzata dall’uso di sistemi pervasivi per
monitorare l’ambiente e modificarlo secondo le esigenze degli utenti e rispettando
vincoli definiti globalmente. Questi sistemi non possono prescindere da requisiti
come la scalabilità e la trasparenza per l’utente. Una tecnologia che consente di
raggiungere questi obiettivi è rappresentata dalle reti di sensori wireless (WSN),
caratterizzate da bassi costi e bassa intrusività . Tuttavia, sebbene in grado di
effettuare elaborazioni a bordo dei singoli nodi, le WSN non hanno da sole le capacitÃ
di elaborazione necessarie a supportare un sistema intelligente; d’altra parte
senza questa attività di pre-elaborazione la mole di dati sensoriali può facilmente
sopraffare un sistema centralizzato con un’eccessiva quantità di dettagli superflui.
Questo lavoro presenta un’architettura cognitiva in grado di percepire e controllare
l’ambiente di cui fa parte, basata su un nuovo approccio per l’estrazione
di conoscenza a partire dai dati grezzi, attraverso livelli crescenti di astrazione.
Le WSN sono utilizzate come strumento sensoriale pervasivo, le cui capacità computazionali
vengono utilizzate per pre-elaborare i dati rilevati, in modo da consentire
ad un sistema centralizzato intelligente di effettuare ragionamenti di alto
livello.
L’architettura proposta è stata utilizzata per sviluppare un testbed dotato degli
strumenti hardware e software necessari allo sviluppo e alla gestione di applicazioni
di AmI basate su WSN, il cui obiettivo principale sia il risparmio energetico. Per
fare in modo che le applicazioni di AmI siano in grado di comunicare con il mondo
esterno in maniera affidabile, per richiedere servizi ad agenti esterni, l’architettura
è stata arricchita con un protocollo di gestione distribuita della reputazione.
È stata inoltre sviluppata un’applicazione di esempio che sfrutta le caratteristiche
del testbed, con l’obiettivo di controllare la temperatura in un ambiente
lavorativo. Quest’applicazione rileva la presenza dell’utente attraverso un modulo
per la fusione di dati multi-sensoriali basato su reti bayesiane, e sfrutta questa
informazione in un controllore fuzzy multi-obiettivo che controlla gli attuatori sulla
base delle preferenze dell’utente e del risparmio energetico.Ambient Intelligence (AmI) systems are characterized by the use of pervasive
equipments for monitoring and modifying the environment according to users’
needs, and to globally defined constraints. Furthermore, such systems cannot ignore
requirements about ubiquity, scalability, and transparency to the user. An
enabling technology capable of accomplishing these goals is represented by Wireless
Sensor Networks (WSNs), characterized by low-costs and unintrusiveness. However,
although provided of in-network processing capabilities, WSNs do not exhibit
processing features able to support comprehensive intelligent systems; on the other
hand, without this pre-processing activities the wealth of sensory data may easily
overwhelm a centralized AmI system, clogging it with superfluous details.
This work proposes a cognitive architecture able to perceive, decide upon, and
control the environment of which the system is part, based on a new approach to
knowledge extraction from raw data, that addresses this issue at different abstraction
levels. WSNs are used as the pervasive sensory tool, and their computational
capabilities are exploited to remotely perform preliminary data processing. A central
intelligent unit subsequently extracts higher-level concepts in order to carry on
symbolic reasoning. The aim of the reasoning is to plan a sequence of actions that
will lead the environment to a state as close as possible to the users’ desires, taking
into account both implicit and explicit feedbacks from the users, while considering
global system-driven goals, such as energy saving. The proposed conceptual architecture
was exploited to develop a testbed providing the hardware and software
tools for the development and management of AmI applications based on WSNs,
whose main goal is energy saving for global sustainability. In order to make the
AmI system able to communicate with the external world in a reliable way, when
some services are required to external agents, the architecture was enriched with
a distributed reputation management protocol.
A sample application exploiting the testbed features was implemented for addressing
temperature control in a work environment. Knowledge about the user’s
presence is obtained through a multi-sensor data fusion module based on Bayesian
networks, and this information is exploited by a multi-objective fuzzy controller
that operates on actuators taking into account users’ preference and energy consumption
constraints
Collaborative autonomy in heterogeneous multi-robot systems
As autonomous mobile robots become increasingly connected and widely deployed in different domains, managing multiple robots and their interaction is key to the future of ubiquitous autonomous systems. Indeed, robots are not individual entities anymore. Instead, many robots today are deployed as part of larger fleets or in teams. The benefits of multirobot collaboration, specially in heterogeneous groups, are multiple. Significantly higher degrees of situational awareness and understanding of their environment can be achieved when robots with different operational capabilities are deployed together. Examples of this include the Perseverance rover and the Ingenuity helicopter that NASA has deployed in Mars, or the highly heterogeneous robot teams that explored caves and other complex environments during the last DARPA Sub-T competition.
This thesis delves into the wide topic of collaborative autonomy in multi-robot systems, encompassing some of the key elements required for achieving robust collaboration: solving collaborative decision-making problems; securing their operation, management and interaction; providing means for autonomous coordination in space and accurate global or relative state estimation; and achieving collaborative situational awareness through distributed perception and cooperative planning. The thesis covers novel formation control algorithms, and new ways to achieve accurate absolute or relative localization within multi-robot systems. It also explores the potential of distributed ledger technologies as an underlying framework to achieve collaborative decision-making in distributed robotic systems.
Throughout the thesis, I introduce novel approaches to utilizing cryptographic elements and blockchain technology for securing the operation of autonomous robots, showing that sensor data and mission instructions can be validated in an end-to-end manner. I then shift the focus to localization and coordination, studying ultra-wideband (UWB) radios and their potential. I show how UWB-based ranging and localization can enable aerial robots to operate in GNSS-denied environments, with a study of the constraints and limitations. I also study the potential of UWB-based relative localization between aerial and ground robots for more accurate positioning in areas where GNSS signals degrade. In terms of coordination, I introduce two new algorithms for formation control that require zero to minimal communication, if enough degree of awareness of neighbor robots is available. These algorithms are validated in simulation and real-world experiments. The thesis concludes with the integration of a new approach to cooperative path planning algorithms and UWB-based relative localization for dense scene reconstruction using lidar and vision sensors in ground and aerial robots
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