231 research outputs found

    A robotic platform for precision agriculture and applications

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    Agricultural techniques have been improved over the centuries to match with the growing demand of an increase in global population. Farming applications are facing new challenges to satisfy global needs and the recent technology advancements in terms of robotic platforms can be exploited. As the orchard management is one of the most challenging applications because of its tree structure and the required interaction with the environment, it was targeted also by the University of Bologna research group to provide a customized solution addressing new concept for agricultural vehicles. The result of this research has blossomed into a new lightweight tracked vehicle capable of performing autonomous navigation both in the open-filed scenario and while travelling inside orchards for what has been called in-row navigation. The mechanical design concept, together with customized software implementation has been detailed to highlight the strengths of the platform and some further improvements envisioned to improve the overall performances. Static stability testing has proved that the vehicle can withstand steep slopes scenarios. Some improvements have also been investigated to refine the estimation of the slippage that occurs during turning maneuvers and that is typical of skid-steering tracked vehicles. The software architecture has been implemented using the Robot Operating System (ROS) framework, so to exploit community available packages related to common and basic functions, such as sensor interfaces, while allowing dedicated custom implementation of the navigation algorithm developed. Real-world testing inside the university’s experimental orchards have proven the robustness and stability of the solution with more than 800 hours of fieldwork. The vehicle has also enabled a wide range of autonomous tasks such as spraying, mowing, and on-the-field data collection capabilities. The latter can be exploited to automatically estimate relevant orchard properties such as fruit counting and sizing, canopy properties estimation, and autonomous fruit harvesting with post-harvesting estimations.Le tecniche agricole sono state migliorate nel corso dei secoli per soddisfare la crescente domanda di aumento della popolazione mondiale. I recenti progressi tecnologici in termini di piattaforme robotiche possono essere sfruttati in questo contesto. Poiché la gestione del frutteto è una delle applicazioni più impegnative, a causa della sua struttura arborea e della necessaria interazione con l'ambiente, è stata oggetto di ricerca per fornire una soluzione personalizzata che sviluppi un nuovo concetto di veicolo agricolo. Il risultato si è concretizzato in un veicolo cingolato leggero, capace di effettuare una navigazione autonoma sia nello scenario di pieno campo che all'interno dei frutteti (navigazione interfilare). La progettazione meccanica, insieme all'implementazione del software, sono stati dettagliati per evidenziarne i punti di forza, accanto ad alcuni ulteriori miglioramenti previsti per incrementarne le prestazioni complessive. I test di stabilità statica hanno dimostrato che il veicolo può resistere a ripidi pendii. Sono stati inoltre studiati miglioramenti per affinare la stima dello slittamento che si verifica durante le manovre di svolta, tipico dei veicoli cingolati. L'architettura software è stata implementata utilizzando il framework Robot Operating System (ROS), in modo da sfruttare i pacchetti disponibili relativi a componenti base, come le interfacce dei sensori, e consentendo al contempo un'implementazione personalizzata degli algoritmi di navigazione sviluppati. I test in condizioni reali all'interno dei frutteti sperimentali dell'università hanno dimostrato la robustezza e la stabilità della soluzione con oltre 800 ore di lavoro sul campo. Il veicolo ha permesso di attivare e svolgere un'ampia gamma di attività agricole in maniera autonoma, come l'irrorazione, la falciatura e la raccolta di dati sul campo. Questi ultimi possono essere sfruttati per stimare automaticamente le proprietà più rilevanti del frutteto, come il conteggio e la calibratura dei frutti, la stima delle proprietà della chioma e la raccolta autonoma dei frutti con stime post-raccolta

    MODELING OF INNOVATIVE LIGHTER-THAN-AIR UAV FOR LOGISTICS, SURVEILLANCE AND RESCUE OPERATIONS

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    An unmanned aerial vehicle (UAV) is an aircraft that can operate without the presence of pilots, either through remote control or automated systems. The first part of the dissertation provides an overview of the various types of UAVs and their design features. The second section delves into specific experiences using UAVs as part of an automated monitoring system to identify potential problems such as pipeline leaks or equipment damage by conducting airborne surveys.Lighter-than-air UAVs, such as airships, can be used for various applications, from aerial photography, including surveying terrain, monitoring an area for security purposes and gathering information about weather patterns to surveillance. The third part reveals the applications of UAVs for assisting in search and rescue operations in disaster situations and transporting natural gas. Using PowerSim software, a model of airship behaviour was created to analyze the sprint-and-drift concept and study methods of increasing the operational time of airships while having a lower environmental impact when compared to a constantly switched-on engine. The analysis provided a reliable percentage of finding the victim during patrolling operations, although it did not account for victim behaviour. The study has also shown that airships may serve as a viable alternative to pipeline transportation for natural gas. The technology has the potential to revolutionize natural gas transportation, optimizing efficiency and reducing environmental impact. Additionally, airships have a unique advantage in accessing remote and otherwise inaccessible areas, providing significant benefits in the energy sector. The employment of this technology was studied to be effective in specific scenarios, and it will be worth continuing to study it for a positive impact on society and the environment

    Robust Model Predictive Control for Linear Parameter Varying Systems along with Exploration of its Application in Medical Mobile Robots

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    This thesis seeks to develop a robust model predictive controller (MPC) for Linear Parameter Varying (LPV) systems. LPV models based on input-output display are employed. We aim to improve robust MPC methods for LPV systems with an input-output display. This improvement will be examined from two perspectives. First, the system must be stable in conditions of uncertainty (in signal scheduling or due to disturbance) and perform well in both tracking and regulation problems. Secondly, the proposed method should be practical, i.e., it should have a reasonable computational load and not be conservative. Firstly, an interpolation approach is utilized to minimize the conservativeness of the MPC. The controller is calculated as a linear combination of a set of offline predefined control laws. The coefficients of these offline controllers are derived from a real-time optimization problem. The control gains are determined to ensure stability and increase the terminal set. Secondly, in order to test the system's robustness to external disturbances, a free control move was added to the control law. Also, a Recurrent Neural Network (RNN) algorithm is applied for online optimization, showing that this optimization method has better speed and accuracy than traditional algorithms. The proposed controller was compared with two methods (robust MPC and MPC with LPV model based on input-output) in reference tracking and disturbance rejection scenarios. It was shown that the proposed method works well in both parts. However, two other methods could not deal with the disturbance. Thirdly, a support vector machine was introduced to identify the input-output LPV model to estimate the output. The estimated model was compared with the actual nonlinear system outputs, and the identification was shown to be effective. As a consequence, the controller can accurately follow the reference. Finally, an interpolation-based MPC with free control moves is implemented for a wheeled mobile robot in a hospital setting, where an RNN solves the online optimization problem. The controller was compared with a robust MPC and MPC-LPV in reference tracking, disturbance rejection, online computational load, and region of attraction. The results indicate that our proposed method surpasses and can navigate quickly and reliably while avoiding obstacles

    Convergence of Intelligent Data Acquisition and Advanced Computing Systems

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    This book is a collection of published articles from the Sensors Special Issue on "Convergence of Intelligent Data Acquisition and Advanced Computing Systems". It includes extended versions of the conference contributions from the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2019), Metz, France, as well as external contributions

    Hybrid Vision and Force Control in Robotic Manufacturing Systems

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    The ability to provide a physical interaction between an industrial robot and a workpiece in the environment is essential for a successful manipulation task. In this context, a wide range of operations such as deburring, pushing, and polishing are considered. The key factor to successfully accomplish such operations by a robot is to simultaneously control the position of the tool-tip of the end-effector and interaction force between the tool and the workpiece, which is a challenging task. This thesis aims to develop new reliable control strategies combining vision and force feedbacks to track a path on the workpiece while controlling the contacting force. In order to fulfill this task, the novel robust hybrid vision and force control approaches are presented for industrial robots subject to uncertainties and interacting with unknown workpieces. The main contributions of this thesis lie in several parts. In the first part of the thesis, a robust cascade vision and force approach is suggested to control industrial robots interacting with unknown workpieces considering model uncertainties. This cascade structure, consisting of an inner vision loop and an outer force loop, avoids the conflict between the force and vision control in traditional hybrid methods without decoupling force and vision systems. In the second part of the thesis, a novel image-based task-sequence/path planning scheme coupled with a robust vision and force control method for solving the multi-task operation problem of an eye-in-hand (EIH) industrial robot interacting with a workpiece is suggested. Each task is defined as tracking a predefined path or positioning to a single point on the workpiece’s surface with a desired interacting force signal, i.e., interaction with the workpiece. The proposed method suggests an optimal task sequence planning scheme to carry out all the tasks and an optimal path planning method to generate a collision-free path between the tasks, i.e., when the robot performs free-motion (pure vision control). In the third part of the project, a novel multi-stage method for robust hybrid vision and force control of industrial robots, subject to model uncertainties is proposed. It aims to improve the performance of the three phases of the control process: a) free-motion using the image-based visual servoing (IBVS) before the interaction with the workpiece; b) the moment that the end-effector touches the workpiece; and c) hybrid vision and force control during the interaction with the workpiece. In the fourth part of the thesis, a novel approach for hybrid vision and force control of eye-in-hand industrial robots is presented which addresses the problem of camera’s field-of-view (FOV) limitation. The merit of the proposed method is that it is capable of expanding the workpiece for eye-in-hand industrial robots to cope with the FOV limitation of the interaction tasks on the workpiece. All the developed algorithms in the thesis are validated via tests on a 6-DOF Denso robot in an eye-in-hand configuration

    Pain phenotypes, sleep problems and other comorbidities in patients with persistent pain

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    The validity of pain intensity as the primary measure in chronic pain treatment has been questioned. It may be important to look at other pain-related factors as well, such as how widespread the pain is or how it interferes with different life activities. These pain-related factors may combine into different pain phenotypes. Understanding how these pain phenotypes associate with, for example, lifestyle and psychological factors may help in targeting treatment better. The aim of this dissertation was to examine pain phenotypes of patients entering tertiary pain care and the features or problems that are associated with them. More specifically, the aims were: · to investigate pain phenotypes with respect to different levels of pain interference and factors associated with them; · to use a data-driven approach to elucidate patient subgroups with different pain phenotypes and investigate factors associated with these, using machine learning methods from a multifactorial set of data; · to elucidate metabolomic markers associated with more severe pain phenotypes by investigating metabolomic markers and pathways with respect to the pain phenotypes found in the previous study, and to two common comorbidities of severe pain, obesity and recurring sleep problems; and · to examine how patients with recurring sleep problems differ from those who sleep normally in the areas of pain and pain-related anxiety, childhood adversities experienced, use of sleep and pain medications, self-reported diseases, and sleep disorders. This was a cross-sectional study. The study data were collected at six pain clinics (three multidisciplinary and three facial pain clinics) in Finland. The whole cohort comprised 473 patients from whom broad data were collected, including sociodemographic factors, previous treatments, comorbidities, lifestyle variables, psychological factors, and others. At multidisciplinary pain clinics with 320 participants, nurses also measured patients’ weight, height, waist circumference, and blood pressure; blood samples were taken for the analysis of metabolomics data. Pain phenotypes combined with levels of pain interference showed adverse changes in different sets of factors when pain interfered highly with either the “activity” or the “affective” dimension. High activity pain interference was associated with reduced exercising, higher body mass index, and higher avoidance of pain, than where both interference dimensions remained low. High affective pain interference was associated with more depression, greater cognitive anxiety, and lower activity engagement when pain was present. When both interference dimensions were high, the previous adverse changes accumulated, smoking was more prevalent, and pain-related anxiety was more pronounced, with fear of pain and physiological anxiety reactions higher than with other pain phenotypes. Data-driven subgrouping of patients resulted in three groups. The groups at the extremes showed pain phenotypes with low pain intensity and pain interference at one end, and combination of high pain intensity, high pain interference and the greatest number of pain sites at the other. In the machine learning analysis, the most informative variables among pain-related factors predicting group membership were affective pain interference and number of pain sites. Of the other factors, sleep problems was the most informative, followed by fear of pain, poorer self-rated health, and lower systolic blood pressure. When metabolomic factors were investigated in relation to pain phenotypes elucidated in the previous data-driven study (least severe pain phenotype in contrast to the two more severe phenotypes combined), obesity, and sleep problems, three metabolomic markers (NAD, AMP, and cysteine) emerged across analyses. Obesity showed association with alterations in amino acid metabolism. Sleep problems were associated with several markers relating to methionine metabolism, which results suggested was downregulated in recurring sleep problems. Patients with recurring sleep problems showed more pain-related anxiety than those sleeping normally, and results suggested physiological anxiety reactions as significant factors for greater difficulties with sleep. Multiple health conditions (for example, asthma and depression) were more prevalent among those with recurring sleep problems. Those with sleep problems reported significantly more restless legs symptoms than those sleeping normally. Having five or more childhood adversities was associated with recurring sleep problems. Finally, the reported use of pain and sleep medications was higher in those with recurring sleep problems than in those who slept normally. To conclude, patient subgroups with varying pain phenotypes were discovered in the studies. Previous studies have highlighted the role of psychosocial factors in those with the most severe pain phenotypes. The results of these studies suggest the importance of sleep and lifestyle-related factors as well. Research into metabolomics may give new insights to why pain becomes more severe for some. Sleep is affected by multiple factors, not only pain, in patients with chronic pain.Kivun voimakkuus ensisijaisena muuttujana on kyseenalaistettu pitkäaikaisen kivun hoidossa. Voi olla tärkeä huomioida myös muut kipumuuttujat, kuten kivun laaja-alaisuus tai kuinka kipu häiritsee erilaisia toimintoja. Yhdessä eri kipumuuttujat voivat muodostaa erilaisia kivun ilmiasuja, fenotyyppejä. Hoidon kohdentamista voi auttaa se, jos ymmärrämme paremmin kuinka erilaiset kivun fenotyypit yhdistyvät esimerkiksi elintapoihin ja psykologisiin tekijöihin. Tämän väitöstutkimuksen aiheena oli tutkia kivun fenotyyppejä ja niihin yhdistyviä tekijöitä potilailla, jotka tulevat pitkäaikaisen kivun hoitoon erikoissairaanhoidossa. Aineisto tähän poikkileikkaustutkimukseen kerättiin kuudella suomalaisella kipuklinikalla. Koko kohortti koostui 473 potilaasta. Heiltä kerättiin tietoa muun muassa taustatekijöistä, aiemmista hoidoista, oheissairauksista, elintavoista sekä erilaisia psykologisia muuttujia. Kivun häiritsevyydestä muodostetut fenotyypit toivat esille, että kivun häiritsevyyden dimensiot (häiritsevyys aktiivisuuteen / affektiivisiin toimintoihin) yhdistyivät eri tekijöihin. Voimakas kivun häiritsevyys aktiivisuuteen yhdistyi vähäisempään vapaa-ajan liikuntaan, korkeampaan painoindeksiin ja esillä oli enemmän kipuun liittyvää välttelykäyttäytymistä verrattuna siihen, että kivun häiritsevyys oli heikkoa kummallakin dimensiolla. Kun sen sijaan kivun häiritsevyys affektiivisiin toimintoihin oli voimakas, esillä oli enemmän depressio-oireita, ahdistunutta ajattelua kipuun liittyen sekä vähemmän kivun hyväksymisestä kertovaa itselle tärkeisiin toimintoihin suuntautumista. Kun kivun häiritsevyys oli voimakas kummallakin dimensiolla, edellä mainitut kielteiset muutokset yhdistyivät. Lisäksi tupakointi oli yleisempää ja kipuun liittyvää ahdistusta oli enemmän, sillä kivun pelkoa ja kehollisia ahdistusreaktioita oli enemmän kuin muissa kivun häiritsevyydestä muodostetuissa fenotyypeissä. Aineistolähtöisesti erottui kolme potilasryhmää erilaisin kivun fenotyypein. Toisessa ääripäässä olevassa ryhmässä kivun voimakkuus ja kivun häiritsevyys olivat matalat, kun taas toisen ääripään ryhmässä yhdistyivät korkea kivun voimakkuus, korkea kivun häiritsevyys ja kipualueiden suuri määrä. Koneoppimisanalyysi toi esille, että uniongelmat olivat keskeinen tekijä suhteessa näihin ryhmiin. Lisäksi kivun pelko, huonoksi koettu terveydentila ja matalampi systolinen verenpaine nousivat analyysissä esille. Analyyseissä löydettiin viitteitä myös ryhmien välisistä eroista metabolomiikassa. Jatkuvista uniongelmista kärsivillä potilailla oli enemmän kipuun liittyvää ahdistusta kuin normaalisti nukkuvilla. Tutkimuksen tulokset viittasivat siihen, että kehollisilla ahdistusreaktioilla oli keskeinen rooli uniongelmissa. Uniongelmista kärsivillä potilailla oli tavallista useammin oheissairauksia (esimerkiksi astma tai depressio). Levottomien jalkojen oireet olivat uniongelmista kärsivillä selkeästi tavallisempia kuin normaalisti nukkuvilla. Uniongelmista kärsivillä oli tavallisemmin taustassaan viisi tai sitä useampi lapsuudenaikainen kuormitustekijä. Uniongelmista kärsivät käyttivät enemmän uni- ja kipulääkkeitä kuin normaalisti nukkuvat. Tutkimuksissa löytyi siis kivun fenotyyppien suhteen erilaisia potilaiden alaryhmiä. Aiemmissa tutkimuksissa on noussut etenkin psykososiaaliset tekijät suhteessa vaikeampiin kivun fenotyyppeihin. Näiden tutkimusten tulokset viittaavat siihen, että uni ja elintapoihin liittyvät tekijät ovat myös tärkeitä. Metabolomiikan tutkimus voi antaa viitteitä aineenvaihdunnallisten prosessien osuudesta kivun vaikeutumisessa. Pitkäaikaisessa kivussa uniongelmiin ei vaikuta pelkkä kipu, vaan myös monet muut tekijät

    Design of autonomous sustainable unmanned aerial vehicle - A novel approach to its dynamic wireless power transfer

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    A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Electric UAVs are presently being used widely in civilian duties such as security, surveillance, and disaster relief. The use of Unmanned Aerial Vehicle (UAV) has increased dramatically over the past years in different areas/fields such as marines, mountains, wild environments. Nowadays, there are many electric UAVs development with fast computational speed and autonomous flying has been a reality by fusing many sensors such as camera tracking sensor, obstacle avoiding sensor, radar sensor, etc. But there is one main problem still not able to overcome which is power requirement for continuous autonomous operation. When the operation needs more power, but batteries can only give for 20 to 30 mins of flight time. These types of system are not reliable for long term civilian operation because we need to recharge or replace batteries by landing the craft every time when we want to continue the operation. The large batteries also take more loads on the UAV which is also not a reliable system. To eliminate these obstacles, there should a recharging wireless power station in ground which can transmit power to these small UAVs wirelessly for long term operation. There will be camera attached in the drone to detect and hover above the Wireless Power Transfer device which got receiving and transmitting station can be use with deep learning and sensor fusion techniques for more reliable flight operations. This thesis explores the use of dynamic wireless power to transfer energy using novel rotating WPT charging technique to the UAV with improved range, endurance, and average speed by giving extra hours in the air. The hypothesis that was created has a broad application beyond UAVs. The drone autonomous charging was mostly done by detecting a rotating WPT receiver connected to main power outlet that served as a recharging platform using deep neural vision capabilities. It was the purpose of the thesis to provide an alternative to traditional self-charging systems that relies purely on static WPT method and requires little distance between the vehicle and receiver. When the UAV camera detect the WPT receiving station, it will try to align and hover using onboard sensors for best power transfer efficiency. Since this strategy relied on traditional automatic drone landing technique, but the target is rotating all the time which needs smart approaches like deep learning and sensor fusion. The simulation environment was created and tested using robot operating system on a Linux operating system using a model of the custom-made drone. Experiments on the charging of the drone confirmed that the intelligent dynamic wireless power transfer (DWPT) method worked successfully while flying on air

    KEER2022

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    Avanttítol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202
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