1,195 research outputs found

    Probabilistic Inference for Model Based Control

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    Robotic systems are essential for enhancing productivity, automation, and performing hazardous tasks. Addressing the unpredictability of physical systems, this thesis advances robotic planning and control under uncertainty, introducing learning-based methods for managing uncertain parameters and adapting to changing environments in real-time. Our first contribution is a framework using Bayesian statistics for likelihood-free inference of model parameters. This allows employing complex simulators for designing efficient, robust controllers. The method, integrating the unscented transform with a variant of information theoretical model predictive control, shows better performance in trajectory evaluation compared to Monte Carlo sampling, easing the computational load in various control and robotics tasks. Next, we reframe robotic planning and control as a Bayesian inference problem, focusing on the posterior distribution of actions and model parameters. An implicit variational inference algorithm, performing Stein Variational Gradient Descent, estimates distributions over model parameters and control inputs in real-time. This Bayesian approach effectively handles complex multi-modal posterior distributions, vital for dynamic and realistic robot navigation. Finally, we tackle diversity in high-dimensional spaces. Our approach mitigates underestimation of uncertainty in posterior distributions, which leads to locally optimal solutions. Using the theory of rough paths, we develop an algorithm for parallel trajectory optimisation, enhancing solution diversity and avoiding mode collapse. This method extends our variational inference approach for trajectory estimation, employing diversity-enhancing kernels and leveraging path signature representation of trajectories. Empirical tests, ranging from 2-D navigation to robotic manipulators in cluttered environments, affirm our method's efficiency, outperforming existing alternatives

    Model learning for trajectory tracking of robot manipulators

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    Abstract Model based controllers have drastically improved robot performance, increasing task accuracy while reducing control effort. Nevertheless, all this was realized with a very strong assumption: the exact knowledge of the physical properties of both the robot and the environment that surrounds it. This assertion is often misleading: in fact modern robots are modeled in a very approximate way and, more important, the environment is almost never static and completely known. Also for systems very simple, such as robot manipulators, these assumptions are still too strong and must be relaxed. Many methods were developed which, exploiting previous experiences, are able to refine the nominal model: from classic identification techniques to more modern machine learning based approaches. Indeed, the topic of this thesis is the investigation of these data driven techniques in the context of robot control for trajectory tracking. In the first two chapters, preliminary knowledge is provided on both model based controllers, used in robotics to assure precise trajectory tracking, and model learning techniques. In the following three chapters, are presented the novelties introduced by the author in this context with respect to the state of the art: three works with the same premise (an inaccurate system modeling), an identical goal (accurate trajectory tracking control) but with small differences according to the specific platform of application (fully actuated, underactuated, redundant robots). In all the considered architectures, an online learning scheme has been introduced to correct the nominal feedback linearization control law. Indeed, the method has been primarily introduced in the literature to cope with fully actuated systems, showing its efficacy in the accurate tracking of joint space trajectories also with an inaccurate dynamic model. The main novelty of the technique was the use of only kinematics information, instead of torque measurements (in general very noisy), to online retrieve and compensate the dynamic mismatches. After that the method has been extended to underactuated robots. This new architecture was composed by an online learning correction of the controller, acting on the actuated part of the system (the nominal partial feedback linearization), and an offline planning phase, required to realize a dynamically feasible trajectory also for the zero dynamics of the system. The scheme was iterative: after each trial, according to the collected information, both the phases were improved and then repeated until the task achievement. Also in this case the method showed its capability, both in numerical simulations and on real experiments on a robotics platform. Eventually the method has been applied to redundant systems: differently from before, in this context the task consisted in the accurate tracking of a Cartesian end effector trajectory. In principle very similar to the fully actuated case, the presence of redundancy slowed down drastically the learning machinery convergence, worsening the performance. In order to cope with this, a redundancy resolution was proposed that, exploiting an approximation of the learning algorithm (Gaussian process regression), allowed to locally maximize the information and so select the most convenient self motion for the system; moreover, all of this was realized with just the resolution of a quadratic programming problem. Also in this case the method showed its performance, realizing an accurate online tracking while reducing both the control effort and the joints velocity, obtaining so a natural behaviour. The thesis concludes with summary considerations on the proposed approach and with possible future directions of research

    Autonomisten metsäkoneiden koneaistijärjestelmät

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    A prerequisite for increasing the autonomy of forest machinery is to provide robots with digital situational awareness, including a representation of the surrounding environment and the robot's own state in it. Therefore, this article-based dissertation proposes perception systems for autonomous or semi-autonomous forest machinery as a summary of seven publications. The work consists of several perception methods using machine vision, lidar, inertial sensors, and positioning sensors. The sensors are used together by means of probabilistic sensor fusion. Semi-autonomy is interpreted as a useful intermediary step, situated between current mechanized solutions and full autonomy, to assist the operator. In this work, the perception of the robot's self is achieved through estimation of its orientation and position in the world, the posture of its crane, and the pose of the attached tool. The view around the forest machine is produced with a rotating lidar, which provides approximately equal-density 3D measurements in all directions. Furthermore, a machine vision camera is used for detecting young trees among other vegetation, and sensor fusion of an actuated lidar and machine vision camera is utilized for detection and classification of tree species. In addition, in an operator-controlled semi-autonomous system, the operator requires a functional view of the data around the robot. To achieve this, the thesis proposes the use of an augmented reality interface, which requires measuring the pose of the operator's head-mounted display in the forest machine cabin. Here, this work adopts a sensor fusion solution for a head-mounted camera and inertial sensors. In order to increase the level of automation and productivity of forest machines, the work focuses on scientifically novel solutions that are also adaptable for industrial use in forest machinery. Therefore, all the proposed perception methods seek to address a real existing problem within current forest machinery. All the proposed solutions are implemented in a prototype forest machine and field tested in a forest. The proposed methods include posture measurement of a forestry crane, positioning of a freely hanging forestry crane attachment, attitude estimation of an all-terrain vehicle, positioning a head mounted camera in a forest machine cabin, detection of young trees for point cleaning, classification of tree species, and measurement of surrounding tree stems and the ground surface underneath.Metsäkoneiden autonomia-asteen kasvattaminen edellyttää, että robotilla on digitaalinen tilannetieto sekä ympäristöstä että robotin omasta toiminnasta. Tämän saavuttamiseksi työssä on kehitetty autonomisen tai puoliautonomisen metsäkoneen koneaistijärjestelmiä, jotka hyödyntävät konenäkö-, laserkeilaus- ja inertia-antureita sekä paikannusantureita. Työ liittää yhteen seitsemässä artikkelissa toteutetut havainnointimenetelmät, joissa useiden anturien mittauksia yhdistetään sensorifuusiomenetelmillä. Työssä puoliautonomialla tarkoitetaan hyödyllisiä kuljettajaa avustavia välivaiheita nykyisten mekanisoitujen ratkaisujen ja täyden autonomian välillä. Työssä esitettävissä autonomisen metsäkoneen koneaistijärjestelmissä koneen omaa toimintaa havainnoidaan estimoimalla koneen asentoa ja sijaintia, nosturin asentoa sekä siihen liitetyn työkalun asentoa suhteessa ympäristöön. Yleisnäkymä metsäkoneen ympärille toteutetaan pyörivällä laserkeilaimella, joka tuottaa lähes vakiotiheyksisiä 3D-mittauksia jokasuuntaisesti koneen ympäristöstä. Nuoret puut tunnistetaan muun kasvillisuuden joukosta käyttäen konenäkökameraa. Lisäksi puiden tunnistamisessa ja puulajien luokittelussa käytetään konenäkökameraa ja laserkeilainta yhdessä sensorifuusioratkaisun avulla. Lisäksi kuljettajan ohjaamassa puoliautonomisessa järjestelmässä kuljettaja tarvitsee toimivan tavan ymmärtää koneen tuottaman mallin ympäristöstä. Työssä tämä ehdotetaan toteutettavaksi lisätyn todellisuuden käyttöliittymän avulla, joka edellyttää metsäkoneen ohjaamossa istuvan kuljettajan lisätyn todellisuuden lasien paikan ja asennon mittaamista. Työssä se toteutetaan kypärään asennetun kameran ja inertia-anturien sensorifuusiona. Jotta metsäkoneiden automatisaatiotasoa ja tuottavuutta voidaan lisätä, työssä keskitytään uusiin tieteellisiin ratkaisuihin, jotka soveltuvat teolliseen käyttöön metsäkoneissa. Kaikki esitetyt koneaistijärjestelmät pyrkivät vastaamaan todelliseen olemassa olevaan tarpeeseen nykyisten metsäkoneiden käytössä. Siksi kaikki menetelmät on implementoitu prototyyppimetsäkoneisiin ja tulokset on testattu metsäympäristössä. Työssä esitetyt menetelmät mahdollistavat metsäkoneen nosturin, vapaasti riippuvan työkalun ja ajoneuvon asennon estimoinnin, lisätyn todellisuuden lasien asennon mittaamisen metsäkoneen ohjaamossa, nuorten puiden havaitsemisen reikäperkauksessa, ympäröivien puiden puulajien tunnistuksen, sekä puun runkojen ja maanpinnan mittauksen

    A New Index for Detecting and Avoiding Type II Singularities for the Control of Non-Redundant Parallel Robots

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    [ES] Los robots paralelos (PR por sus siglas en inglés) son mecanismos donde el efector final está unido a la base, mediante al menos dos cadenas cinemáticas abiertas. Los PRs ofrecen una gran capacidad de carga y alta precisión, lo que los hace adecuados para diversas aplicaciones, entre ellas la interacción persona-robot. Sin embargo, en las proximidades de una singularidad Tipo II (singularidad dentro del espacio de trabajo), un PR pierde el control sobre los movimientos del efector final. La pérdida de control representa un riesgo importante para los usuarios, especialmente en rehabilitación robótica. En las últimas décadas, los PR se han popularizado en la rehabilitación de miembros inferiores debido al aumento del número de personas que viven con limitaciones físicas. Así, esta tesis trata sobre la detección y evitación de singularidades de Tipo II para asegurar total control de un PR no redundante para la rehabilitación y diagnóstico de rodilla, denominado 3UPS+RPU. En la literatura, existen varios índices para detectar y medir la cercanía a una singularidad basados en métodos analíticos y geométricos. Sin embargo, algunos de estos índices carecen de significado físico y son incapaces de identificar los actuadores responsables de la pérdida de control. Esta tesis aporta dos novedosos índices para detectar y medir la proximidad a una singularidad de Tipo II, capaces de identificar el par de actuadores responsables de la singularidad. Los dos índices son los ángulos entre los componentes lineal (T_i,j) y angular (O_i,j) de dos Twist Screw de Salida (OTS por sus siglas en inglés) normalizados i,j. Una singularidad Tipo II es detectada cuando T_i,j = O_i,j = 0 y su proximidad se mide mediante los mínimos ángulos T_i,j (minT) y O_i,j (minO) para los casos plano y espacial, respectivamente. La eficacia de los índices T_i,j y O_i,j se evalúa de forma teórica y experimental en un robot 3UPS+RPU y un mecanismo de cinco barras. Además, se propone un procedimiento experimental para el adecuado establecimiento del límite de cercanía a una singularidad de Tipo II mediante la aproximación progresiva del PR a una singularidad y la medición de la última posición controlable. Posteriormente, se desarrollan dos nuevos algoritmos deterministas para liberar y evitar una singularidad de Tipo II basados en minT y minO para PR no redundantes. minT y minO se utilizan para identificar los dos actuadores a mover para liberar o evitar el PR de una singularidad. Ambos algoritmos requieren una medición precisa de la pose alcanzada por el efector final. El algoritmo para liberar un PR de una configuración singular se aplica con éxito en un controlador híbrido basado en visión artificial para el PR 3UPS+RPU. El controlador utiliza un sistema de fotogrametría para medir la pose del robot debido a la degeneración del modelo cinemático en las proximidades de una singularidad. El algoritmo de evasión de singularidades Tipo II se aplica a la planificación offline y online de trayectorias no singulares para un mecanismo de cinco barras y el PR 3UPS+RPU. Estas aplicaciones verifican el bajo coste computacional y la mínima desviación introducida en la trayectoria original por los nuevos algoritmos. La implementación directa de un controlador de fuerza/posición en el PR 3UPS+RPU es insegura porque el paciente podría llevar involuntariamente al PR a una singularidad. Por lo tanto, esta tesis concluye presentando un novedoso controlador de fuerza/posición complementado con el algoritmo de evasión de singularidades de Tipo II. El nuevo controlador se evalúa durante rehabilitación activa de una pierna de maniquí y una pierna humana no lesionada. Los resultados muestran que el nuevo controlador combinado mantiene el PR 3UPS+RPU lejos de configuraciones singulares con una desviación mínima de la trayectoria original. Por lo tanto, esta tesis habilita el 3UPS+RPU PR para la rehabilitación segura de miembros inferiores lesionados.[CAT] Els robots paral·lels (PR per les seues sigles en anglés) són mecanismes on l'efector final està unit a la base, mitjançant almenys dues cadenes cinemàtiques obertes. Els PRs ofereixen una gran capacitat de càrrega i alta precisió, la qual cosa els fa adequats per a diverses aplicacions, entre elles la interacció persona-robot. No obstant això, en les proximitats d'una singularitat Tipus II (singularitat dins de l'espai de treball), un PR perd el control sobre els moviments de l'efector final. La pèrdua de control representa un risc important per als usuaris, especialment en rehabilitació robòtica. En les últimes dècades, els PR s'han popularitzat en la rehabilitació de membres inferiors a causa de l'augment del nombre de persones que viuen amb limitacions físiques. Així, aquesta tesi tracta sobre la detecció i evació de singularitats de Tipus II per a assegurar total control d'un PR no redundant per a la rehabilitació i diagnòstic de genoll, denominat 3UPS+RPU. En la literatura, existeixen diversos índexs per a detectar i mesurar la proximitat a una singularitat basats en mètodes analítics i geomètrics. No obstant això, alguns d'aquests índexs manquen de significat físic i són incapaços d'identificar els actuadors responsables de la pèrdua de control. Aquesta tesi aporta dos nous índexs per a detectar i mesurar la proximitat a una singularitat de Tipus II, capaços d'identificar el parell d'actuadors responsables de la singularitat. Els dos índexs són els angles entre els components lineal (T_i,j) i angular (O_i,j) de dues Twist Screw d'Eixida (OTS per les seues sigles en engonals) normalitzats i,j. Una singularitat Tipus II és detectada quan T_i,j = O_i,j = 0 i la seua proximitat es mesura mitjançant els minimos angles T_i,j (minT) i O_i,j (minO) per als casos pla i espacial, respectivament. L'eficàcia dels índexs T_i,j i O_i,j es evalua de manera teòrica i experimental en un robot 3UPS+RPU i un mecanisme de cinc barres. A més, es proposa un procediment experimental per a l'adequat establiment del límit de proximitat a una singularitat de Tipus II mitjançant l'aproximació progressiva del PR a una singularitat i el mesurament de l'última posició controlable. Posteriorment, es desenvolupen dos nous algorismes deterministes per a alliberar i evadir una singularitat de Tipus II basats en minT i minO per a PR no redundants. minT i minO s'utilitzen per a identificar els dos actuadors a moure per a alliberar o evadir el PR d'una singularitat. Aquests algorismes requereixen un mesurament precís de la posa aconseguida per l'efector final. L'algorisme per a alliberar un PR d'una configuració singular s'aplica amb èxit en un controlador híbrid basat en visió artificial per al PR 3UPS+RPU. El controlador utilitza un sistema de fotogrametria per a mesurar la posa del robot a causa de la degeneració del model cinemàtic en les proximitats d'una singularitat. L'algorisme d'evació de singularitats Tipus II s'aplica a la planificació offline i en línia de trajectòries no singulars per a un mecanisme de cinc barres i el PR 3UPS+RPU. Aquestes aplicacions verifiquen el baix cost computacional i la mínima desviació introduïda en la trajectòria original pels nous algorismes. La implementació directa d'un controlador de força/posició en el PR 3UPS+RPU és insegura perquè el pacient podria portar involuntàriament al PR a una singularitat. Per tant, aquesta tesi conclou presentant un nou controlador de força/posició complementat amb l'algorisme d'evació de singularitats de Tipus II. El nou controlador s'avalua durant la rehabilitació activa d'una cama de maniquí i una cama humana no lesionada. Els resultats mostren que el nou controlador combinat manté el PR 3UPS+RPU lluny de configuracions singulars amb una desviació mínima de la trajectòria original. Per tant, aquesta tesi habilita el 3UPS+RPU PR per a la rehabilitació segura dels membres inferiors lesionats.[EN] Parallel Robots (PR)s are mechanisms where the end-effector is linked to the base by at least two open kinematics chains. The PRs offer a high payload and high accuracy, making them suitable for various applications, including human robot interaction. However, in proximity to a Type II singularity (singularity within the workspace), a PR loses control over the movements of the end-effector. The loss of control represents a major risk for users, especially in robotic rehabilitation. In the last decades, PRs have become popular in lower limb rehabilitation because of the increment in the number of people living with physical limitations. Thus, this thesis is about the detection and avoidance of Type II singularities to ensure complete control of a non-redundant PR for knee rehabilitation and diagnosis named 3UPS+RPU. In the literature, several indices exist to detect and measure the closeness to a singular configuration based on analytical and geometrical methods. However, some of these indices have no physical meaning, and they are unable to identify the actuators responsible for the loss of control. This thesis contributes two novel indices to detect and measure the proximity to a Type II singularity capable of identifying the pair of actuators responsible for the singularity. The two indices are the angles between the linear (T_i,j) and the angular (O_i,j) components of two i,j normalised Output Twist Screws (OTSs). A Type II singularity is detected when the angles T_i,j = O_i,j = 0 and its closeness is measured by the minimum T_i,j (minT) and minimum O_i,j (minO) for planar and spatial cases, respectively. The effectiveness of the indices T_i,j and O_i,j is evaluated from a theoretical and experimental perspective in a 3UPS+RPU and a five bars mechanism. Moreover, an experimental procedure is proposed for setting a proper limit of closeness to a Type II singularity by the progressive approach of the PR to singular configuration and measuring the last controllable pose. Subsequently, two novel deterministic algorithms for releasing and avoiding Type II singularities based on minT and minO are developed for non-redundant PRs. The minT and minO are used to identify the two actuators to move for release or prevent the PR from the singularity. Both algorithms require an accurate measuring of the pose reached by the end-effector. The algorithm to release a PR from a singular configuration is successfully applied in a vision-based hybrid controller for the 3UPS+RPU PR. The controller uses a photogrammetry system to measure the pose of the robot due to the degeneration of the kinematic model in the vicinity of a singularity. The Type II singularity avoidance algorithm is applied to offline and online free-singularity trajectory planning for a five-bar mechanism and the 3UPS+RPU PR. These applications verify the low computation cost and the minimum deviation introduced in the original trajectory for both novel algorithms. The direct implementation of a force/position controller in the 3UPS+RPU PR is unsafe because the patient could unintentionally drive the PR to a Type II singularity. Therefore, this thesis concludes by presenting a novel force/position controller complemented with the Type II singularity avoidance algorithm. The complemented controller is evaluated during patient-active exercises in a mannequin leg and an uninjured human limb. The results show that the novel combined controller keeps the 3UPS+RPU PR far from singular configurations with a minimum deviation on the original trajectory. Hence, this thesis enables the 3UPS+RPU PR for the safe rehabilitation of injured lower limbs.Pulloquinga Zapata, JL. (2023). A New Index for Detecting and Avoiding Type II Singularities for the Control of Non-Redundant Parallel Robots [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19427

    Path and Motion Planning for Autonomous Mobile 3D Printing

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    Autonomous robotic construction was envisioned as early as the ‘90s, and yet, con- struction sites today look much alike ones half a century ago. Meanwhile, highly automated and efficient fabrication methods like Additive Manufacturing, or 3D Printing, have seen great success in conventional production. However, existing efforts to transfer printing technology to construction applications mainly rely on manufacturing-like machines and fail to utilise the capabilities of modern robotics. This thesis considers using Mobile Manipulator robots to perform large-scale Additive Manufacturing tasks. Comprised of an articulated arm and a mobile base, Mobile Manipulators, are unique in their simultaneous mobility and agility, which enables printing-in-motion, or Mobile 3D Printing. This is a 3D printing modality, where a robot deposits material along larger-than-self trajectories while in motion. Despite profound potential advantages over existing static manufacturing-like large- scale printers, Mobile 3D printing is underexplored. Therefore, this thesis tack- les Mobile 3D printing-specific challenges and proposes path and motion planning methodologies that allow this printing modality to be realised. The work details the development of Task-Consistent Path Planning that solves the problem of find- ing a valid robot-base path needed to print larger-than-self trajectories. A motion planning and control strategy is then proposed, utilising the robot-base paths found to inform an optimisation-based whole-body motion controller. Several Mobile 3D Printing robot prototypes are built throughout this work, and the overall path and motion planning strategy proposed is holistically evaluated in a series of large-scale 3D printing experiments

    Exploring Robot Teleoperation in Virtual Reality

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    This thesis presents research on VR-based robot teleoperation with a focus on remote environment visualisation in virtual reality, the effects of remote environment reconstruction scale in virtual reality on the human-operator's ability to control the robot and human-operator's visual attention patterns when teleoperating a robot from virtual reality. A VR-based robot teleoperation framework was developed, it is compatible with various robotic systems and cameras, allowing for teleoperation and supervised control with any ROS-compatible robot and visualisation of the environment through any ROS-compatible RGB and RGBD cameras. The framework includes mapping, segmentation, tactile exploration, and non-physically demanding VR interface navigation and controls through any Unity-compatible VR headset and controllers or haptic devices. Point clouds are a common way to visualise remote environments in 3D, but they often have distortions and occlusions, making it difficult to accurately represent objects' textures. This can lead to poor decision-making during teleoperation if objects are inaccurately represented in the VR reconstruction. A study using an end-effector-mounted RGBD camera with OctoMap mapping of the remote environment was conducted to explore the remote environment with fewer point cloud distortions and occlusions while using a relatively small bandwidth. Additionally, a tactile exploration study proposed a novel method for visually presenting information about objects' materials in the VR interface, to improve the operator's decision-making and address the challenges of point cloud visualisation. Two studies have been conducted to understand the effect of virtual world dynamic scaling on teleoperation flow. The first study investigated the use of rate mode control with constant and variable mapping of the operator's joystick position to the speed (rate) of the robot's end-effector, depending on the virtual world scale. The results showed that variable mapping allowed participants to teleoperate the robot more effectively but at the cost of increased perceived workload. The second study compared how operators used a virtual world scale in supervised control, comparing the virtual world scale of participants at the beginning and end of a 3-day experiment. The results showed that as operators got better at the task they as a group used a different virtual world scale, and participants' prior video gaming experience also affected the virtual world scale chosen by operators. Similarly, the human-operator's visual attention study has investigated how their visual attention changes as they become better at teleoperating a robot using the framework. The results revealed the most important objects in the VR reconstructed remote environment as indicated by operators' visual attention patterns as well as their visual priorities shifts as they got better at teleoperating the robot. The study also demonstrated that operators’ prior video gaming experience affects their ability to teleoperate the robot and their visual attention behaviours

    Study and Development of Mechatronic Devices and Machine Learning Schemes for Industrial Applications

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    Obiettivo del presente progetto di dottorato è lo studio e sviluppo di sistemi meccatronici e di modelli machine learning per macchine operatrici e celle robotizzate al fine di incrementarne le prestazioni operative e gestionali. Le pressanti esigenze del mercato hanno imposto lavorazioni con livelli di accuratezza sempre più elevati, tempi di risposta e di produzione ridotti e a costi contenuti. In questo contesto nasce il progetto di dottorato, focalizzato su applicazioni di lavorazioni meccaniche (e.g. fresatura), che includono sistemi complessi quali, ad esempio, macchine a 5 assi e, tipicamente, robot industriali, il cui utilizzo varia a seconda dell’impiego. Oltre alle specifiche problematiche delle lavorazioni, si deve anche considerare l’interazione macchina-robot per permettere un’efficiente capacità e gestione dell’intero impianto. La complessità di questo scenario può evidenziare sia specifiche problematiche inerenti alle lavorazioni (e.g. vibrazioni) sia inefficienze più generali che riguardano l’impianto produttivo (e.g. asservimento delle macchine con robot, consumo energetico). Vista la vastità della tematica, il progetto si è suddiviso in due parti, lo studio e sviluppo di due specifici dispositivi meccatronici, basati sull’impiego di attuatori piezoelettrici, che puntano principalmente alla compensazione di vibrazioni indotte dal processo di lavorazione, e l’integrazione di robot per l’asservimento di macchine utensili in celle robotizzate, impiegando modelli di machine learning per definire le traiettorie ed i punti di raggiungibilità del robot, al fine di migliorarne l’accuratezza del posizionamento del pezzo in diverse condizioni. In conclusione, la presente tesi vuole proporre soluzioni meccatroniche e di machine learning per incrementare le prestazioni di macchine e sistemi robotizzati convenzionali. I sistemi studiati possono essere integrati in celle robotizzate, focalizzandosi sia su problematiche specifiche delle lavorazioni in macchine operatrici sia su problematiche a livello di impianto robot-macchina. Le ricerche hanno riguardato un’approfondita valutazione dello stato dell’arte, la definizione dei modelli teorici, la progettazione funzionale e l’identificazione delle criticità del design dei prototipi, la realizzazione delle simulazioni e delle prove sperimentali e l’analisi dei risultati.The aim of this Ph.D. project is the study and development of mechatronic systems and machine learning models for machine tools and robotic applications to improve their performances. The industrial demands have imposed an ever-increasing accuracy and efficiency requirement whilst constraining the cost. In this context, this project focuses on machining processes (e.g. milling) that include complex systems such as 5-axes machine tool and industrial robots, employed for various applications. Beside the issues related to the machining process itself, the interaction between the machining centre and the robot must be considered for the complete industrial plant’s improvement. This scenario´s complexity depicts both specific machining problematics (e.g. vibrations) and more general issues related to the complete plant, such as machine tending with an industrial robot and energy consumption. Regarding the immensity of this area, this project is divided in two parts, the study and development of two mechatronic devices, based on piezoelectric stack actuators, for the active vibration control during the machining process, and the robot machine tending within the robotic cell, employing machine learning schemes for the trajectory definition and robot reachability to improve the corresponding positioning accuracy. In conclusion, this thesis aims to provide a set of solutions, based on mechatronic devices and machine learning schemes, to improve the conventional machining centre and the robotic systems performances. The studied systems can be integrated within a robotic cell, focusing on issues related to the specific machining process and to the interaction between robot-machining centre. This research required a thorough study of the state-of-the-art, the formulation of theoretical models, the functional design development, the identification of the critical aspects in the prototype designs, the simulation and experimental campaigns, and the analysis of the obtained results

    From visuomotor control to latent space planning for robot manipulation

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    Deep visuomotor control is emerging as an active research area for robot manipulation. Recent advances in learning sensory and motor systems in an end-to-end manner have achieved remarkable performance across a range of complex tasks. Nevertheless, a few limitations restrict visuomotor control from being more widely adopted as the de facto choice when facing a manipulation task on a real robotic platform. First, imitation learning-based visuomotor control approaches tend to suffer from the inability to recover from an out-of-distribution state caused by compounding errors. Second, the lack of versatility in task definition limits skill generalisability. Finally, the training data acquisition process and domain transfer are often impractical. In this thesis, individual solutions are proposed to address each of these issues. In the first part, we find policy uncertainty to be an effective indicator of potential failure cases, in which the robot is stuck in out-of-distribution states. On this basis, we introduce a novel uncertainty-based approach to detect potential failure cases and a recovery strategy based on action-conditioned uncertainty predictions. Then, we propose to employ visual dynamics approximation to our model architecture to capture the motion of the robot arm instead of the static scene background, making it possible to learn versatile skill primitives. In the second part, taking inspiration from the recent progress in latent space planning, we propose a gradient-based optimisation method operating within the latent space of a deep generative model for motion planning. Our approach bypasses the traditional computational challenges encountered by established planning algorithms, and has the capability to specify novel constraints easily and handle multiple constraints simultaneously. Moreover, the training data comes from simple random motor-babbling of kinematically feasible robot states. Our real-world experiments further illustrate that our latent space planning approach can handle both open and closed-loop planning in challenging environments such as heavily cluttered or dynamic scenes. This leads to the first, to our knowledge, closed-loop motion planning algorithm that can incorporate novel custom constraints, and lays the foundation for more complex manipulation tasks

    Safe navigation and human-robot interaction in assistant robotic applications

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