1,720,992 research outputs found
Learning-based methods for Robotic control
Full text linkRobots nowadays are being employed in increasingly complex scenarios, where the number of possible assumptions that can be made to ease the control synthesis is getting considerably smaller compared to the past. In fact, back in the day control engineers could heavily rely on a static world assumption and on a perfect knowledge of the system dynamics, since robots were practically only confined in controlled assembly lines where everything was predetermined beforehand. Given these premises, it was fairly easy to synthesize control laws able to solve with high precision the programmed task. Recently, task complexity started to grow considerably with respect to the past, requiring a new type of controller able to adapt continuously to the unknown scenarios to be faced. Among all the new methods, learning-based control can be considered one of the most promising approaches in literature today.
This thesis investigates the use of this new control technique in robotics. We start by giving some background materials on Machine Learning, discussing how we can learn a better dynamical model for the robot just from sensor data, or even directly synthesize a control law from experiences. Then, after a small excursus on Optimal Control we present our contributions in this novel field.
Specifically, a learning-based feedback linearization controller is proposed to deal with model uncertainties in fully actuated robots. This novel technique is then extended to underactuated systems, where control is tremendously complicated by the impossibility in these robots to follow arbitrary trajectories which are not dynamically feasible, i.e. not generated by an exact knowledge of their models.
Finally, we present a contribution in the field of Reinforcement Learning, an approach that is able to learn directly a controller for a given task just by a trial and error mechanism. As detailed in the first chapters, Reinforcement Learning does not assure arbitrary constraints satisfaction in the final learned controller, which limits tremendously its applicability on real platforms. For this aspect, we propose an online mechanism where Optimal Control is used to enhance the safety of the final control law
Control techniques for collaborative and cooperative robotic systems
Full text linkOvertheyears, theindustrial landscapehasexperiencedsignificantevolutionsdrivenby advancementsintechnology,economicfluctuations,shiftsinsocietalandenvironmental dynamics,andevolvingconsumerpreferences.Thesechangeshaveresultedinfundamental alterations inthewaybusinessesoperate, impactingvariousaspectsof the industry, includingtheadoptionoftechnology, laborpractices,businessmodels,andsustainability initiatives. Theever-changingindustrial environment ismarkedbyseveral emergingfrontiers thatembraceontheonehandtheincorporationofdigitaltechnologies,cyber-physical systems,artificial intelligence,andtheinternetofthingsintomanufacturingprocesses withthepotential for futuredevelopments, suchas Industry5.0,whichemphasizes theadvancementofcollaborativeroboticsystems. Industry5.0, inparticular,placesa significantemphasisonhuman-robotcollaboration(HRC)byvaluinghumaninput. Ontheotherhand, theleading-edgefrontiersinvolveincorporatingsensorsanddata analyticsintointelligentinfrastructure,whichservestoelevatemaintenancestandards, minimizedowntime,andenhancesafety.Thisalsoentailsleveragingcooperativerobotmachinesystems, suchasdronesanddiagnostictrains, for infrastructure inspections. Thesemeasurescontributetocostreductionandefficiencyenhancementinthemonitoring andmaintenanceprocesses. Intheindustrialsector,arealmofnewprospectsisemerging, drivenbytheinnovativedevelopmentof last-miledeliverysolutions,encompassingdrone deliveries,autonomousdeliveryvehicles,andsmart lockers,alldesignedtostreamline urbanlogistics. Asaresult, thisthesis isdedicatedtosolvingtwoof themost importantresearch challenges indesigningdecisionandcontrol techniquesforcollaborativeandcooperative roboticsystemsandinparticularforHRCandaerial-groundmobileroboticsystems. In thefirst part, this thesis aims toaddress the gaps identified in the existing literatureregardingsafe,ergonomic,andefficientHRC,whichhavebeenbroughttolight throughacomprehensivereviewconductedinthisfield. Inparticular, thedeveloped contributionsregardtheconceptualizationanddevelopmentofnovelarchitecturesand controltechniquesforHRC, inpresenceorabsenceofoptimization.Thecentralaimisto concurrentlyoptimizethethreekeyobjectives, i.e.,safety,ergonomics,andefficiencyin tasksassociatedwithaddressingthetrajectoryplanningproblemformulatedassecondorder coneprogrammingproblemand solvedwith thedirect transcriptionmethod, whilerespectingthespeedandseparationmonitoring(SSM)ISOsafetyrequirementand guaranteeingtheergonomicoptimalpositionof theoperatorduringthecollaborative phase.ExpandingupontheessentialcriteriaforasafeandergonomicHRCtoencompass theemergingdomainofcollaborationbetweenhumananddrone,thesecondgoal involves creatingcontrolalgorithms, i.e., linearquadraticregulator (LQR)controllers, forsystems involvinghumansanddroneswithinindoorindustrialsettingslikewarehouses4.0. Thesecondpartofthisthesis isfocusedonthecooperationbetweenafleetofdrones oranindividualdroneandagroundmobileroboticsystem(i.e., train, truck)thatentails theseentitiesworkinginharmonytoachievespecificobjectivesortasks inacoordinated manner.Particularemphasisisplacedonthecriticalphaseofdronesreturningtoand landingonamovingtrainor truck. Thus, ad-hoccontrol techniques, i.e., consensus algorithm, LQR and receding horizon LQR controllers, are presented to tackle such complext asks in an efficient and effective way
Model predictive control of cyber-physical systems
No full textCyber-Physical Systems (CPS) represent a groundbreaking technological advancement that integrates physical processes with computational resources and networking capabilities, heralding a significant leap in efficiency, functionality, and adaptability across various applications. From revolutionizing transportation through self-driving cars to enhancing energy distribution via smart grids, CPS are poised to be pivotal in the fourth industrial revolution, fundamentally altering daily life and work in a manner akin to the transformative impacts of the internet and the World Wide Web.
Originating from the concept of merging digital and physical realms, CPS aim to create systems that are inherently intelligent, adaptive, and resilient, extending beyond traditional embedded systems by leveraging advancements in computing, communication, and control.
These systems are characterized by a core architecture comprising physical components (sensors and actuators), cyber elements (computational and communication infrastructure), and control mechanisms (algorithms and software), working in unison through a feedback loop to dynamically interact with and respond to their environment. In this respect, the work done in this thesis is the application of Model Predictive Control (MPC) framework to CPS with the aim to an increase in operational efficiency, an increase in optimality with respect to resource allocation, and an increase in general responsiveness and adaptability of such systems to ambient variability. This thesis attempts to manifest the future implications in applying MPC to transform the management and control of these sophisticated cyber-physical systems within their crucial sectors via theoretical development and practical implementation of case studies.
The control methodology discussed in this thesis regards the application of MPC in three case studies: in the frame of Power Systems, Smart Cities and Industry 4.0 in the space sector.
The first work deals with the emerging complexities in modern transmission and distribution grids that arise through integration with distributed energy resources such as electric energy storage systems, renewable energy plants, and plug-in electric vehicles. The new issues are the intermittency in power generation from renewable sources and in the demand from electric vehicles present a new challenge to grids requiring advancement in grid control and optimization. Considering these challenges, in this work the candidate proposes a novel reconfiguration algorithm based on MPC for the dynamic configuration and re-configuration (topology) of the grid to minimize losses and to improve operational resilience in the presence of adverse events like faults or (cyber-)attacks. The algorithm progresses over the existing methods by removing the necessity of constantly connected grids to let autonomous grid islands be formed that can dynamically get connected and disconnected from the main grid. This research provides a critical review of existing network reconfiguration strategies, spanning between classic optimization-based methods, heuristics ((meta)heuristics), and machine learning-based solutions with their respective advantages and limits. It is hence observed that while the classic optimization methods actually give optimum solutions, they are afflicted by high computational costs. (Meta)heuristics are computationally efficient, though void of guarantees about the optimality of solutions. Machine learning based approaches, in particular Reinforcement Learning, promise policies that are near optimal but come at an enormously high demand for computational resources during training and also offer serious concerns about safety. In such a way, the proposed MPC-based solution combines the features of optimal control at a lower computational cost and adaptability for real-time applications. This means to be the breakthrough approach in network reconfiguration, bridging the gaps that exist within today's available methodologies and thereby offering a powerful, robust, efficient, flexible solution to meet challenges posed by today's modern, dynamic grid environment.
The second work addresses a crucial challenge that urban greenhouse gases (GHG), primarily produced by buildings and transportation, with a focus on optimization of the intelligent traffic light (TL) control systems in mitigating road congestion. Given the global climate change efforts like the 2016 Paris Agreement and the EU 2019 Green Deal, the study would emphasize the need for viable urban traffic management strategies that could lead to significant GHG emission reductions, as a majority of such emissions originate from urban settings. Although an extensive literature on Intelligent TL controls is available today, it is found that there is a gap in adaptability and efficiency, mainly in real-time traffic conditions. A novel model predictive control strategy based on mixed-integer optimization has been proposed in this thesis to enhance the timings of TLs at intersections by an original approach different from classical fixed-timing strategies without any real-time reaction. The main contribution of this thesis lies in proposing an integrated MPC controller which determines both the optimal signal timing for the TLs and optimal trajectories for Automatically Driven Vehicles (ADVs), while modelling also Manually Driven Vehicles (MDVs) dynamics, leading to significant reduction of queue length and waiting times. In these terms the controller is adaptive, allowing it to operate in mixed scenarios. In addition, several innovative constraints that have been introduced within the MPC formulation allow recursive feasibility to be ensured in constraint-activating events, for instance, when a vehicle approaching the TL during red signal could bring the problem towards infeasibility because some constraints cannot be violated.
About Industry 4.0, the most important challenge this thesis tackles is the optimization of task scheduling and controlling in the spaceport within the dynamically changing space industry, which previously limited to governmental entities is now expanding to include private companies. This research was carried out within the framework of the H2020 SESAME project--partnership led by ArianeGroup--that aims to enhance the schedule of assembly operations of space vehicles to maximize the launch throughput at the Guiana space center in Kourou. In the literature they are referred to as Assembly Line Balancing Problems (ALBP) and the key contribution of this work is the development of a scalable MPC algorithm, integrated with a Mixed-Integer Linear Program (MILP) model, to optimize campaign planning in real-time leverages both static and dynamic data, addressing scalability, flexibility and the ability to manage complex constraints, and real-time disturbances. Simulation results confirm the merit of proposed efficient task scheduling algorithm which retains the characteristics of standard MPC and outperforms state-of-the-art optimal scheduling heuristics maintaining similar speed which makes it suitable for real-time implementation
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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