Probabilistic Localization of a Soccer Robot

Mobiilsed autonoomsed robotid vajavad iseseisvaks navigeerimiseks teadmist oma umbkaudse asukoha kohta. Tihtipeale pole see otseselt tuvastatav, vaid roboti positsioon tuleb järeldada mitmete müraste sensorite mõõtmistest. Antud tees tegeleb probleemiga, kuidas lokaliseerida iseseisvat jalgpallirobotit videopildi alusel. Kasutatakse statistilisi Bayesi filtreerimise meetodeid nagu Kalmani- ja osakeste filter, mis arvestavad sellistele süsteemidele omase müra ja ebakindlusega. Implementeeritakse ja võrreldakse mitmeid erinevaid lokalisatsioonialgoritme ja testitakse neid ka lisaks simulaatorile ka füüsilise roboti peal. Töötatakse välja toimiv praktiline lahendus mobiilse jalgpalliroboti lokaliseerimiseks.The thesis deals with the problem of localizing a mobile soccer-playing robot using Bayes filtering methods. For navigating natural environments, autonomous robots need to know where they are located even if the position of the robot is not directly observable, but rather needs to be inferred from indirect measurements of several noisy sensors. The algorithms need to account for the inherent uncertainty of such systems. Several algorithms of robot positioning including Kalman filter and particle filter are investigated, implemented and compared. The algorithms are also tested on a real robot. A working solution for practical robot localization is developed

On Robotic Work-Space Sensing and Control

Industrial robots are fast and accurate when working with known objects at precise locations in well-structured manufacturing environments, as done in the classical automation setting. In one sense, limited use of sensors leaves robots blind and numb, unaware of what is happening in their surroundings. Whereas equipping a system with sensors has the potential to add new functionality and increase the set of uncertainties a robot can handle, it is not as simple as that. Often it is difficult to interpret the measurements and use them to draw necessary conclusions about the state of the work space. For effective sensor-based control, it is necessary to both understand the sensor data and to know how to act on it, giving the robot perception-action capabilities. This thesis presents research on how sensors and estimation techniques can be used in robot control. The suggested methods are theoretically analyzed and evaluated with a large focus on experimental verification in real-time settings. One application class treated is the ability to react fast and accurately to events detected by vision, which is demonstrated by the realization of a ball-catching robot. A new approach is proposed for performing high-speed color-based image analysis that is robust to varying illumination conditions and motion blur. Furthermore, a method for object tracking is presented along with a novel way of Kalman-filter initialization that can handle initial-state estimates with infinite variance. A second application class treated is robotic assembly using force control. A study of two assembly scenarios is presented, investigating the possibility of using force-controlled assembly in industrial robotics. Two new approaches for robotic contact-force estimation without any force sensor are presented and validated in assembly operations. The treated topics represent some of the challenges in sensor-based robot control, and it is demonstrated how they can be used to extend the functionality of industrial robots

Optimized Acoustic Sensing for Fixed-Wing Uncrewed Aerial Vehicles

Acoustic sensors are devices that are not commonly used on autonomous uncrewed aerial vehicles (UAV). Obtaining a usable signal-to-noise ratio (SNR) is challenging. Given the most problematic noise is the flight-induced wind noise, one way of approaching the problem is to stop the wind noise at the source by designing a mount for the acoustic sensors to reduce the wind component before the signal and noise enter the microphone. Subsequently, signal processing stages can be added to improve the SNR further. We begin by formulating an atmospheric attenuation model using both point and line acoustic sources. The model predicts the frequency spectrum and how it reacts to changes in atmospheric conditions. This model is used to predict the SNR over the frequency range of interest as measured at the UAV for various wind speeds for a given acoustic source sound pressure level (SPL) as well as predict the SNR as a function of distance. Multiple fixed-wing UAV mounting strategies are then developed based on the predicted airflow during flight with each analyzed with respect to SNR. Based on predicted SNRs, various signal processing algorithms are evaluated for their improvement of detection statistics. Finally, the SNR of the processed signal is evaluated for usability. Particular instantiations of the acoustic sensing wing mounts are evaluated in the lab using a wind tunnel as well as in some physical UAV test flights. Data collected from these flights is processed offline using different signal processing approaches. Based on the model predictions and the results of the limited field measurements, conclusions regarding the feasibility of acoustic sensing on a UAV are discussed

Null models and complexity science: disentangling signal from noise in complex interacting systems

The constantly increasing availability of fine-grained data has led to a very detailed description of many socio-economic systems (such as financial markets, interbank loans or supply chains), whose representation, however, quickly becomes too complex to allow for any meaningful intuition or insight about their functioning mechanisms. This, in turn, leads to the challenge of disentangling statistically meaningful information from noise without assuming any a priori knowledge on the particular system under study. The aim of this thesis is to develop and test on real world data unsupervised techniques to extract relevant information from large complex interacting systems. The question I try to answer is the following: is it possible to disentangle statistically relevant information from noise without assuming any prior knowledge about the system under study? In particular, I tackle this challenge from the viewpoint of hypothesis testing by developing techniques based on so-called null models, i.e., partially randomised representations of the system under study. Given that complex systems can be analysed both from the perspective of their time evolution and of their time-aggregated properties, I have tested and developed one technique for each of these two purposes. The first technique I have developed is aimed at extracting “backbones” of relevant relationships in complex interacting systems represented as static weighted networks of pairwise interactions and it is inspired by the well-known Pólya urn combinatorial process. The second technique I have developed is instead aimed at identifying statistically relevant events and temporal patterns in single or multiple time series by means of maximum entropy null models based on Ensemble Theory. Both of these methodologies try to exploit the heterogeneity of complex systems data in order to design null models that are tailored to the systems under study, and therefore capable of identifying signals that are genuinely distinctive of the systems themselves

Estimating motion and time to contact in 3D environments: Priors matter

[eng] Until the present moment, an extensive amount of research has been done on how humans estimate motion or parameters of a task, such as the timeto- contact in simple scenarios. However, most avoid questioning how we extract 3D information from 2D optic information. A Bayesian approach based on a combination of optic and prior knowledge about statistical regularities of the environment would allow solving the ambiguity when translating 2D into 3D estimates. The present dissertation aims to analyse if the estimation of motion and time-to-contact in complex 3D environments is compatible with a combination of visual and prior information. In the first study, we analyse the predictions of a Bayesian model with a preference for slow speeds to estimate the direction of an object. The information available to judge movement in depth is much less precise than information about the lateral movement. Thus, combining both sources of information with a prior with preference for low speeds, estimates of motion in depth will be proportionally more attracted to low speeds than estimates of lateral motion. Thus, the perceived direction would depend on stimulus speed when estimating the ball’s direction. Our experimental results showed that the bias in perceived direction increased at higher speeds, which would be congruent with increasingly less precise motion estimates (consistent with Weber’s law). In the second study, we analyse the existing evidence on using a priori knowledge of the Earth’s gravitational acceleration and the size of objects to estimate the time to contact in parabolic trajectories. We analysed the existing evidence for using knowledge of the Earth’s gravity and the size of an object in the interaction with the surrounding environment. Next, we simulate predictions of the GS model. This model allows predicting the time to contact based on a combination of a priori variables (gravity and ball size) and optic variables. We compare the accuracy of the predictions of time-to-contact with an alternative only using optic variables, showing that relying on priors of gravitation and ball size solves the ambiguity in the estimation of the time-to-contact. Finally, we offer scenarios where the GS model would lead to predictions with systematic errors, which we will test in the following studies. In the third study, we created trajectories for which the GS model gives accurate predictions of the time to contact at different flight times but provides different systematic errors at any other time. We hypothesized that if the ball’s visibility is restricted to a short time window, the participants would prefer to see the ball during the time windows in which the model predictions are accurate. Our results showed that observers preferred to use a relatively constant ball viewing time. However, we showed evidence that the direction of the errors made by the participants for the different trajectories tested corresponded to the direction predicted by the GS model. In the fourth and final study, we investigated the role of a priori knowledge of the Earth’s gravitational acceleration and ball size in estimating the time of flight and the direction of motion of an observer towards the interception point. We introduced our participants in an environment where both gravitational acceleration and ball size was randomized trial-to-trial. The observers’ task was to move towards the interception point and predict the remaining flight time after a short occlusion. Our results provide evidence for using prior knowledge of gravity and ball size to estimate the time-to-contact. We also find evidence that gravitational acceleration may play a role in guiding locomotion towards the interception point. In summary, in this thesis, we contribute to answering a fundamental question in Perception: how we interpret information to act in the world. To do so, we show evidence that humans apply their knowledge about regularities in the environment in the form of a priori knowledge of the Earth’s gravitational acceleration, the size of the ball, or that objects stand still in the world when interpreting visual information.[spa] Hasta el momento, se ha realizado una gran cantidad de investigación sobre como el ser humano estima el movimiento o los parámetros de una tarea como el tiempo de contacto en escenarios simples. Sin embargo, la mayoría evita preguntarse cómo se extrae la información 3D a partir de la información óptica 2D. Un enfoque bayesiano basado en una combinación de información óptica y a priori sobre regularidades estadísticas del entorno interiorizadas en forma de conocimiento permitiría resolver la ambigüedad a la hora de traducir claves ópticas en 2D a estimaciones sobre propiedades del mundo en 3D. El objetivo de esta tesis es analizar si la estimación del movimiento y del tiempo de contacto en entornos 3D complejos es compatible con una combinación de información visual y a priori. En el primer estudio, se analizan las predicciones de un modelo bayesiano con preferencia por las velocidades lentas para la estimación de la dirección de un objeto. La información disponible para juzgar el movimiento en profundidad es mucho menos precisa que la información sobre el movimiento lateral. Así, cuando se combinan ambas fuentes de información con un prior con preferencia por la velocidad baja, las estimaciones del movimiento en profundidad serán proporcionalmente más atraídas por el prior que las estimaciones del movimiento lateral. Por lo tanto, la dirección percibida dependería de la velocidad del estímulo. Nuestros resultados experimentales mostraron que el sesgo en la dirección percibida aumentaba a velocidades más altas, lo que sería congruente con estimaciones de movimiento cada vez menos precisas (consistente con la ley de Weber). En el segundo estudio, analizamos las evidencias existentes sobre el uso del conocimiento a priori de la aceleración gravitatoria de la Tierra y el tamaño de los objetos para estimar el tiempo de contacto en trayectorias parabólicas. Analizamos las pruebas existentes sobre el uso del conocimiento de la gravedad de la Tierra y el tamaño de un objeto en la interacción con el entorno. A continuación, simulamos las predicciones del modelo GS, un modelo que permite predecir el tiempo de contacto a partir de una combinación de variables a priori (gravedad y tamaño de pelota) y variables ópticas. Comparamos la precisión de las predicciones del tiempo de contacto con una alternativa que solo utiliza variables ópticas, mostrando que basarse en las variables a priori de la gravedad y el tamaño de la bola resuelve la ambigüedad en la estimación del tiempo de contacto. Por último, mostramos varios escenarios en los que el modelo GS conduciría a predicciones con errores sistemáticos; escenarios que pondremos a prueba en los siguientes estudios. En el tercer estudio, creamos trayectorias para las que el modelo GS da predicciones precisas del tiempo hasta el contacto en diferentes tiempos de vuelo, pero proporciona diferentes errores sistemáticos en cualquier otro momento. Hipotetizamos que, si la visibilidad de la pelota está restringida a una ventana de tiempo corta, los participantes preferirían ver la pelota durante las ventanas de tiempo en las que las predicciones del modelo son precisas. Nuestros resultados mostraron que los observadores preferían utilizar un tiempo de visualización de la pelota relativamente constante. Por otra parte, mostramos pruebas de que la dirección de los errores cometidos por los participantes para las diferentes trayectorias probadas se correspondía con la dirección predicha por el modelo GS. En el cuarto y último estudio, investigamos el papel del conocimiento a priori de la aceleración gravitatoria de la Tierra y del tamaño de la pelota en la estimación del tiempo de vuelo y la dirección de movimiento de un observador hacia el punto de interceptación. Introdujimos a nuestros participantes en un entorno en el que tanto la aceleración gravitatoria como el tamaño de la pelota se asignaban aleatoriamente ensayo a ensayo. La tarea de los observadores consistía en desplazarse hacia el punto de interceptación y predecir el tiempo de vuelo restante tras una breve oclusión. Nuestros resultados proporcionan pruebas del uso del conocimiento previo de la gravedad y el tamaño de la pelota para estimar el tiempo de contacto. También encontramos pruebas de que la aceleración gravitatoria puede desempeñar un papel en la orientación de la locomoción hacia el punto de intercepción. En resumen, en esta tesis contribuimos a responder a una cuestión fundamental en la Percepción: como interpretamos la información para actuar en el mundo. Para ello, mostramos evidencias de que los humanos aplican sus conocimientos sobre regularidades del entorno en forma de conocimiento a priori de la aceleración gravitatoria de la tierra, del tamaño de la pelota o de la estabilidad del mundo a nuestro alrededor para interpretar la información visual

Robotic Work-Space Sensing and Control

Industrial robots are traditionally programmed using only the internal joint position sensors, in a sense leaving the robot blind and numb. Using external sensors, such as cameras and force sensors, allows the robot to detect the existence and position of objects in an unstructured environment, and to handle contact situations not possible using only position control. This thesis presents work on how external sensors can be used in robot control. A vision-based robotic ball-catcher was implemented, showing how high-speed computer vision can be used for robot control with hard time constraints. Special attention is payed to tracking of a flying ball with an arbitrary number of cameras, how to initialize the tracker when no information about the initial state is available, and how to dynamically update the robot trajectory when the end point of the trajectory is modified due to new measurements. In another application example, force control was used to perform robotic assembly. It is shown how force sensing can be used to handle uncertain position

Fast Decision-making under Time and Resource Constraints

Practical decision makers are inherently limited by computational and memory resources as well as the time available in which to make decisions. To cope with these limitations, humans actively seek methods which limit their resource demands by exploiting structure within the environment and exploiting a coupling between their sensing and actuation to form heuristics for fast decision-making. To date, such behavior has not been replicated in artificial agents. This research explores how heuristics may be incorporated into the decision-making process to quickly make high-quality decisions through the analysis of a prominent case study: the outfielder problem. In the outfielder problem, a fielder is required to intercept balls traveling in ballistic trajectories, while the motion of the fielder is constrained to the ground plane. In order to maximize the probability of interception, the agent must make good, yet timely, decisions. Researchers have put forth several heuristic approaches to describe how a fielder may decide how to run based only on immediately available information under different control paradigms. This research statistically quantifies upper bounds on the expected catch rate of a couple notable approaches, given that interception of the ball is theoretically possible if the fielder ran directly towards the landing spot with maximal effort throughout the entire duration of the ball’s flight. Additionally, novel modifications are made to a belief-space variant of iterative Linear Quadratic Gaussian (iLQG), which is an online method that may be used to find locally-optimal policies to continuous Partially Observable Markov Decision Processes (POMDPs) in which Bayesian estimation may reasonably be approximated by an Extended Kalman Filter (EKF). Directional derivatives are used to reduce the computation time of certain matrix derivatives with respect to the variance of the belief state from to , where is the dimension of the belief space. However, the improved algorithm still may not be capable of real-time decision-making by the standards of modern-day computing on mobile platforms, especially in systems with long planning horizons and sparse rewards. The belief-space variant of iLQG is applied to the outfielder problem, which may also indicate its applicability to similar target interception problems with input constraints, such as missile defense

Proceedings of Mathsport international 2017 conference

Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017. MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet. Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports

Data Hiding in Digital Video

With the rapid development of digital multimedia technologies, an old method which is called steganography has been sought to be a solution for data hiding applications such as digital watermarking and covert communication. Steganography is the art of secret communication using a cover signal, e.g., video, audio, image etc., whereas the counter-technique, detecting the existence of such as a channel through a statistically trained classifier, is called steganalysis. The state-of-the art data hiding algorithms utilize features; such as Discrete Cosine Transform (DCT) coefficients, pixel values, motion vectors etc., of the cover signal to convey the message to the receiver side. The goal of embedding algorithm is to maximize the number of bits sent to the decoder side (embedding capacity) with maximum robustness against attacks while keeping the perceptual and statistical distortions (security) low. Data Hiding schemes are characterized by these three conflicting requirements: security against steganalysis, robustness against channel associated and/or intentional distortions, and the capacity in terms of the embedded payload. Depending upon the application it is the designer\u27s task to find an optimum solution amongst them. The goal of this thesis is to develop a novel data hiding scheme to establish a covert channel satisfying statistical and perceptual invisibility with moderate rate capacity and robustness to combat steganalysis based detection. The idea behind the proposed method is the alteration of Video Object (VO) trajectory coordinates to convey the message to the receiver side by perturbing the centroid coordinates of the VO. Firstly, the VO is selected by the user and tracked through the frames by using a simple region based search strategy and morphological operations. After the trajectory coordinates are obtained, the perturbation of the coordinates implemented through the usage of a non-linear embedding function, such as a polar quantizer where both the magnitude and phase of the motion is used. However, the perturbations made to the motion magnitude and phase were kept small to preserve the semantic meaning of the object motion trajectory. The proposed method is well suited to the video sequences in which VOs have smooth motion trajectories. Examples of these types could be found in sports videos in which the ball is the focus of attention and exhibits various motion types, e.g., rolling on the ground, flying in the air, being possessed by a player, etc. Different sports video sequences have been tested by using the proposed method. Through the experimental results, it is shown that the proposed method achieved the goal of both statistical and perceptual invisibility with moderate rate embedding capacity under AWGN channel with varying noise variances. This achievement is important as the first step for both active and passive steganalysis is the detection of the existence of covert channel. This work has multiple contributions in the field of data hiding. Firstly, it is the first example of a data hiding method in which the trajectory of a VO is used. Secondly, this work has contributed towards improving steganographic security by providing new features: the coordinate location and semantic meaning of the object

Random media and processes estimation using non-linear filtering techniques: application to ensemble weather forecast and aircraft trajectories

L'erreur de prédiction d'une trajectoire avion peut être expliquée par différents facteurs. Les incertitudes associées à la prévision météorologique sont l'un d'entre-eux. Qui plus est, l'erreur de prévision de vent a un effet non négligeable sur l'erreur de prédiction de la position d'un avion. En regardant le problème sous un autre angle, il s'avère que les avions peuvent être utilisés comme des capteurs locaux pour estimer l'erreur de prévision de vent. Dans ce travail nous décrivons ce problème d'estimation à l'aide de plusieurs processus d'acquisition d'un même champ aléatoire. Quand ce champ est homogène, nous montrons que le problème est équivalent à plusieurs processus aléatoires évoluant dans un même environnement aléatoire pour lequel nous donnons un modèle de Feynman-Kac. Nous en dérivons une approximation particulaire et fournissons pour les estimateurs obtenus des résultats de convergence. Quand le champ n'est pas homogène mais qu'une décomposition en sous-domaine homogène est possible, nous proposons un modèle différent basé sur le couplage de plusieurs processus d'acquisition. Nous en déduisons un modèle de Feynman-Kac et suggérons une approximation particulaire du flot de mesure. Par ailleurs, pour pouvoir traiter un trafic aérien, nous développons un modèle de prédiction de trajectoire avion. Finalement nous démontrons dans le cadre de simulations que nos algorithmes peuvent estimer les erreurs de prévisions de vent en utilisant les observations délivrées par les avions le long de leur trajectoire.Aircraft trajectory prediction error can be explained by different factors. One of them is the weather forecast uncertainties. For example, the wind forecast error has a non negligible impact on the along track accuracy for the predicted aircraft position. From a different perspective, that means that aircrafts can be used as local sensors to estimate the weather forecast error. In this work we describe the estimation problem as several acquisition processes of a same random field. When the field is homogeneous, we prove that they are equivalent to random processes evolving in a random media for which a Feynman-Kac formulation is done. Then we give a particle-based approximation and provide convergence results of the ensuing estimators. When the random field is not homogeneous but can be decomposed in homogeneous sub-domains, a different model is proposed based on the coupling of different acquisition processes. From there, a Feynman-Kac formulation is derived and its particle-based approximation is suggested. Furthermore, we develop an aircraft trajectory prediction model. Finally we demonstrate on a simulation set-up that our algorithms can estimate the wind forecast errors using the aircraft observations delivered along their trajectory