1,619 research outputs found

    A method for the assessment and compensation of positioning errors in industrial robots

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    Industrial Robots (IR) are currently employed in several production areas as they enable flexible automation and high productivity on a wide range of operations. The IR low positioning performance, however, has limited their use in high precision applications, namely where positioning errors assume importance for the process and directly affect the quality of the final products. Common approaches to increase the IR accuracy rely on empirical relations which are valid for a single IR model. Also, existing works show no uniformity regarding the experimental procedures followed during the IR performance assessment and identification phases. With the aim to overcome these restrictions and further extend the IR usability, this paper presents a general method for the evaluation of IR pose and path accuracy, primarily focusing on instrumentation and testing procedures. After a detailed description of the experimental campaign carried out on a KUKA KR210 R2700 Prime robot under different operating conditions (speed, payload and temperature state), a novel online compensation approach is presented and validated. The position corrections are processed with an industrial PC by means of a purposely developed application which receives as input the position feedback from a laser tracker. Experiments conducted on straight paths confirmed the validity of the proposed approach, which allows remarkable reductions (in the order of 90%) of the orthogonal deviations and in-line errors during the robot movements

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    Mirror-Aware Neural Humans

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    Human motion capture either requires multi-camera systems or is unreliable using single-view input due to depth ambiguities. Meanwhile, mirrors are readily available in urban environments and form an affordable alternative by recording two views with only a single camera. However, the mirror setting poses the additional challenge of handling occlusions of real and mirror image. Going beyond existing mirror approaches for 3D human pose estimation, we utilize mirrors for learning a complete body model, including shape and dense appearance. Our main contributions are extending articulated neural radiance fields to include a notion of a mirror, making it sample-efficient over potential occlusion regions. Together, our contributions realize a consumer-level 3D motion capture system that starts from off-the-shelf 2D poses by automatically calibrating the camera, estimating mirror orientation, and subsequently lifting 2D keypoint detections to 3D skeleton pose that is used to condition the mirror-aware NeRF. We empirically demonstrate the benefit of learning a body model and accounting for occlusion in challenging mirror scenes.Comment: Project website: https://danielajisafe.github.io/mirror-aware-neural-humans

    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

    Navigation Sensor Stochastic Error Modeling and Nonlinear Estimation for Low-Cost Land Vehicle Navigation

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    The increasing use of low-cost inertial sensors in various mass-market applications necessitates their accurate stochastic modeling. Such task faces challenges due to outliers in the sensor measurements caused by internal and/or external factors. To optimize the navigation performance, robust estimation techniques are required to reduce the influence of outliers to the stochastic modeling process. The Generalized Method of Wavelet Moments (GMWM) and its Multi-signal extensions (MS-GMWM) represent the latest trend in the field of inertial sensor error stochastic analysis, they are capable of efficiently modeling the highly complex random errors displayed by low-cost and consumer-grade inertial sensors and provide very advantageous guarantees for the statistical properties of their estimation products. On the other hand, even though a robust version exists (RGMWM) for the single-signal method in order to protect the estimation process from the influence of outliers, their detection remains a challenging task, while such attribute has not yet been bestowed in the multi-signal approach. Moreover, the current implementation of the GMWM algorithm can be computationally intensive and does not provide the simplest (composite) model. In this work, a simplified implementation of the GMWM-based algorithm is presented along with techniques to reduce the complexity of the derived stochastic model under certain conditions. Also, it is shown via simulations that using the RGMWM every time, without the need for contamination existence confirmation, is a worthwhile trade-off between reducing the outlier effects and decreasing the estimator efficiency. Generally, stochastic modeling techniques, including the GMWM, make use of individual static signals for inference. However, it has been observed that when multiple static signal replicates are collected under the same conditions, they maintain the same model structure but exhibit variations in parameter values, a fact that called for the MS-GMWM. Here, a robust multi-signal method is introduced, based on the established GMWM framework and the Average Wavelet Variance (AWV) estimator, which encompasses two robustness levels: one for protection against outliers in each considered replicate and one to safeguard the estimation against the collection of signal replicates with significantly different behaviour than the majority. From that, two estimators are formulated, the Singly Robust AWV (SR-AWV) and the Doubly Robust (DR-AWV) and their model parameter estimation efficiency is confirmed under different data contamination scenarios in simulation and case studies. Furthermore, a hybrid case study is conducted that establishes a connection between model parameter estimation quality and implied navigation performance in those data contamination settings. Finally, the performance of the new technique is compared to the conventional Allan Variance in a land vehicle navigation experiment, where the inertial information is fused with an auxiliary source and vehicle movement constraints using the Extended and Unscented Kalman Filters (EKF/UKF). Notably, the results indicate that under linear-static conditions, the UKF with the new method provides a 16.8-17.3% improvement in 3D orientation compared to the conventional setting (AV with EKF), while the EKF gives a 7.5-9.7% improvement. Also, in dynamic conditions (i.e., turns), the UKF demonstrates an 14.7-17.8% improvement in horizontal positioning and an 11.9-12.5% in terms of 3D orientation, while the EKF has an 8.3-12.8% and an 11.4-11.7% improvement respectively. Overall, the UKF appears to perform better but has a significantly higher computational load compared to the EKF. Hence, the EKF appears to be a more realistic option for real-time applications such as autonomous vehicle navigation

    Various Applications of Methods and Elements of Adaptive Optics

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    This volume is focused on a wide range of topics, including adaptive optic components and tools, wavefront sensing, different control algorithms, astronomy, and propagation through turbulent and turbid media

    Aerial Drone-based System for Wildfire Monitoring and Suppression

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    Wildfire, also known as forest fire or bushfire, being an uncontrolled fire crossing an area of combustible vegetation, has become an inherent natural feature of the landscape in many regions of the world. From local to global scales, wildfire has caused substantial social, economic and environmental consequences. Given the hazardous nature of wildfire, developing automated and safe means to monitor and fight the wildfire is of special interest. Unmanned aerial vehicles (UAVs), equipped with appropriate sensors and fire retardants, are available to remotely monitor and fight the area undergoing wildfires, thus helping fire brigades in mitigating the influence of wildfires. This thesis is dedicated to utilizing UAVs to provide automated surveillance, tracking and fire suppression services on an active wildfire event. Considering the requirement of collecting the latest information of a region prone to wildfires, we presented a strategy to deploy the estimated minimum number of UAVs over the target space with nonuniform importance, such that they can persistently monitor the target space to provide a complete area coverage whilst keeping a desired frequency of visits to areas of interest within a predefined time period. Considering the existence of occlusions on partial segments of the sensed wildfire boundary, we processed both contour and flame surface features of wildfires with a proposed numerical algorithm to quickly estimate the occluded wildfire boundary. To provide real-time situational awareness of the propagated wildfire boundary, according to the prior knowledge of the whole wildfire boundary is available or not, we used the principle of vector field to design a model-based guidance law and a model-free guidance law. The former is derived from the radial basis function approximated wildfire boundary while the later is based on the distance between the UAV and the sensed wildfire boundary. Both vector field based guidance laws can drive the UAV to converge to and patrol along the dynamic wildfire boundary. To effectively mitigate the impacts of wildfires, we analyzed the advancement based activeness of the wildfire boundary with a signal prominence based algorithm, and designed a preferential firefighting strategy to guide the UAV to suppress fires along the highly active segments of the wildfire boundary

    Electron Thermal Runaway in Atmospheric Electrified Gases: a microscopic approach

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    Thesis elaborated from 2018 to 2023 at the Instituto de Astrofísica de Andalucía under the supervision of Alejandro Luque (Granada, Spain) and Nikolai Lehtinen (Bergen, Norway). This thesis presents a new database of atmospheric electron-molecule collision cross sections which was published separately under the DOI : With this new database and a new super-electron management algorithm which significantly enhances high-energy electron statistics at previously unresolved ratios, the thesis explores general facets of the electron thermal runaway process relevant to atmospheric discharges under various conditions of the temperature and gas composition as can be encountered in the wake and formation of discharge channels

    Shaped-based IMU/Camera Tightly Coupled Object-level SLAM using Rao-Blackwellized Particle Filtering

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    Simultaneous Localization and Mapping (SLAM) is a decades-old problem. The classical solution to this problem utilizes entities such as feature points that cannot facilitate the interactions between a robot and its environment (e.g., grabbing objects). Recent advances in deep learning have paved the way to accurately detect objects in the image under various illumination conditions and occlusions. This led to the emergence of object-level solutions to the SLAM problem. Current object-level methods depend on an initial solution using classical approaches and assume that errors are Gaussian. This research develops a standalone solution to object-level SLAM that integrates the data from a monocular camera and an IMU (available in low-end devices) using Rao Blackwellized Particle Filter (RBPF). RBPF does not assume Gaussian distribution for the error; thus, it can handle a variety of scenarios (such as when a symmetrical object with pose ambiguities is encountered). The developed method utilizes shape instead of texture; therefore, texture-less objects can be incorporated into the solution. In the particle weighing process, a new method is developed that utilizes the Intersection over the Union (IoU) area of the observed and projected boundaries of the object that does not require point-to-point correspondence. Thus, it is not prone to false data correspondences. Landmark initialization is another important challenge for object-level SLAM. In the state-of-the-art delayed initialization, the trajectory estimation only relies on the motion model provided by IMU mechanization (during the initialization), leading to large errors. In this thesis, two novel undelayed initializations are developed. One relies only on a monocular camera and IMU, and the other utilizes an ultrasonic rangefinder as well. The developed object-level SLAM is tested using wheeled robots and handheld devices, and an error (in the position) of 4.1 to 13.1 cm (0.005 to 0.028 of the total path length) has been obtained through extensive experiments using only a single object. These experiments are conducted in different indoor environments under different conditions (e.g. illumination). Further, it is shown that undelayed initialization using an ultrasonic sensor can reduce the algorithm's runtime by half

    Bluff-body aerodynamics and transfer functions for non-catching precipitation measurement instruments.

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    Starting from the old and trivial technique of using a graduated cylinder to collect and manually measure precipitation, numerous advances were made for in-situ precipitation gauges. After decades of scarce innovation, a new family of in-situ precipitation gauges was developed. They are called Non-Catching Gauges (NCG) since they can measure precipitation and its microphysical and dynamic characteristics without the need to collect hydrometeors. The attention that NCGs are gathering today is quite notable, even if they represent only a small fraction of the total precipitation gauges deployed. Their use in the field is bound to continuously grow in time, due to several advantages, discussed in this work, that such instruments present over more traditional ones. However, their major disadvantage is their increased complexity, the effects of which are highlighted by the literature through evidence of calibration and correction issues. Various field intercomparison experiments showed the evidence of significant biases in NCGs measurements. The goal of this work is to investigate two main sources of bias, producing the largest impact on precipitation measurements. The first source of bias evaluated in this work is due to instrument calibration. Several attempts at developing a calibration procedure are presented both in the scientific literature and from the manufacturers. Nevertheless, those methods are hardly traceable to international standards and, in most cases, lack a suitable reference measure to compare against the instrumental output. In this work, a fully traceable calibration procedure is proposed, in analogy with the one already existing for catching type gauges. This requires drops of know diameter and fall velocity to be released over the instrument sensing area. For this reason, the Calibrated Rainfall Generator (CRG) is developed, able to release single drops on demand and measure them independently just before they reach the instrument sensing area. Detachment of drops is obtained by using an electrostatic system, while the measure of their diameter and fall velocity is performed by means of a photogrammetric approach. The Thies Laser Precipitation Monitor (LPM) was tested using the CRG considering two different output telegrams. The first one provides the raw measure of each drop sensed by the instrument while the second one provides the Particle Size and fall Velocity Distribution (PSVD) matrix. Both telegrams show a tendency to underestimate the drop diameter that increases with decreasing the drop size, while errors in the fall velocity measurements have a less definite trend. Furthermore, tests also show a large standard deviation of the measurements, significantly higher than the one of the reference measurements. The underestimation of drop size and fall velocity is also reflected into the RI measurements provided by the instrument, with a resulting underestimation that decreases with increasing the precipitation intensity. The difference between the two telegrams considered is large and may only be explained by differences in the instrument internal processing for the two telegrams. The second instrument tested using the CRG is the Biral VPF-750, a light scatter gauge. Results show a tendency to underestimate both the drop diameter and fall velocity. In the first case, the error decreases with increasing the drops size, similarly to the Thies LPM. However, the error in the fall velocity is considerably higher and instead increases with increasing the drop sizes. In terms of Rainfall Intensity (RI), the instrument shows a strong underestimation that, due to the opposite trend observed for drop diameter and fall velocity, is almost constant with the precipitation intensity. Both instruments show significant biases, corroborated by field intercomparison results from the literature, that is often larger than 10% for the investigated variables. This means that both gauges cannot be classified according to the guidelines proposed in this work for the development of a standard calibration procedure, derived from those already existing for CGs. The second source of bias is wind, a well-established source of environmental error for traditional Catching-type Gauges (CG) but also affecting NCGs. The wind-induced bias is investigated using a numerical approach, combining Computational Fluid Dynamics (CFD) and Lagrangian Particle Tracking (LPT) models. Two different CFD models were tested, the first providing a time-independent steady state solution, while the other is fully time-dependent. Both were compared against wind tunnel results, showing a good agreement with the experimental data, and proving their ability to capture the complex aerodynamic response of instruments when impacted by the wind. The Thies Laser Precipitation Monitor (LPM) is first chosen as a test instrument, being representative of the typical NCGs that are currently deployed in the field. CFD simulations show that wind direction is the primary factor determining the aerodynamic disturbance close to the instrument sensing area. Similar results were found for the OTT Parsivel2, that is another widely diffused NCG. For wind flow parallel to the laser beam, strong disturbance close to the gauge sensing area is observed. Meanwhile, wind coming perpendicular to the laser beam produces minimal flow disturbance. The wind-induced bias is also investigated for the Vaisala WXT-520, an impact disdrometer. This gauge is smaller ad has a more regular shape if compared to the optical disdrometers, but its measuring principle is based on the detection of the drop kinetic energy, while the size and fall velocity are indirectly obtained. CFD simulations show limited disturbance close to the sensing area of the instrument and a negligeable dependency on the wind direction (due to a more radially symmetric geometry). The instrument body further provide minimal shielding of the sensing area. Strong updraft however occurs upstream of the instrument for all wind directions, significantly affecting the fall velocity of the smaller and lighter drops. Using these results, three different LPT models are also tested. The first is an uncoupled model based on the time-independent CFD results and is used to evaluate the instrument performance for all wind speeds and directions considered. The other two models, due to their high computational requirements, are applied only to a selected number of combinations of wind speed and direction for the Thies LPM. Results show a good agreement and allow concluding that the significant increase in computational burden of the latter two models does not significantly improve the accuracy of the results. However, the one-way coupled model highlights the role of turbulence, that may have a significant impact on the instrumental performance when strong recirculation is present near its sensing area. In the case of the two other gauges, only the uncoupled LPT model in combination with the time-independent CFD model is used, this being the best compromise between numerical accuracy and computational cost. Results of the LPT model are presented in terms of variation in the retrieval of precipitation microphysical properties, Catch Ratios (CR), Collection Efficiency (CE) and Radar Retrieval Efficiency (RRE). For the three gauges considered, it is shown that smaller hydrometeors fall velocity close to the instrument sensing area is strongly affected by wind and is – in general – reduced. A significant wind-induced bias is also evident in the Drop Size Distribution (DSD) measured by the gauges. Optical gauges may report a significant lower number of small hydrometeors even at moderate wind speed. Due to the gauge body partially shielding the sensing area. Impact gauge DSD is also strongly influenced by wind, since hydrometeors with high kinetic energy are sensed as having a large diameter. The DSD is therefore shifted towards larger diameters and the instrument tends to overestimate the number of hydrometeors of all sizes. This suggests that the different shapes of the DSD function reported in the field by different instruments may be due, at least partially, to wind-induced biases. In terms of integral precipitation characteristics, the wind direction is the primary factor in determining the performance of optical gauges in windy conditions. For wind parallel to the laser beam, the instrument senses less and less precipitation with increasing the wind speed, with no hydrometeors even reaching the sensing area in some configurations . On the other hand, when the wind is perpendicular to the laser beam, the instrument performs similarly for all wind speeds, with CR and CE values close to one and only a moderate amount of overcatch being observed at high wind speed. Only for the OTT Parsivel2 a non negligeable overcatch is also evident for wind coming at a 45° angle with respect to the beam direction. For the Vaisala WXT-520 the Kinetic Catch Ratio (KCR) and Kinetic Collection Efficiency (KCE) are defined as substitutes for the CR and CE. At low wind speed, the KCR is below unity, due to the reduction in fall velocity produced by the updraft. However, with increasing wind speed, the kinetic energy of hydrometeors carried by wind increases considerably, overcoming the reduction caused by the updraft close to the gauge. For this reason, KCR values becomes much higher than unity, especially for small size hydrometeors. The increase in kinetic energy is reflected into increased KCE values, that are close to unity at low wind speed, but rapidly grow with increasing the wind speed. Wind direction has instead very limited influence on the measurements. In terms of RRE, optical gauges present limited bias for all combinations of wind speed and direction, except for the highest wind speed and flow parallel to the laser beam. This is because a large portion of the radar reflectivity factor (dBZ) is due to medium and large size hydrometeors, that are less influenced by wind. In the case of the impact disdrometer instead, RRE behaves very similarly to the CE, with values that increases with increasing wind speed. This is due to the shift toward larger diameters noted in the DSD that occurs when hydrometeors kinetic energy is increased by wind
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