Evidenzkarten-basierte Sensorfusion zur Umfelderkennung und Interpretation in der Ernte

Korthals T, Skiba A, Krause T, Jungeblut T. Evidenzkarten-basierte Sensorfusion zur Umfelderkennung und Interpretation in der Ernte. In: Ruckelshausen A, Meyer-Aurich A, Rath T, Recke G, Theuvsen B, eds. Informatik in der Land-, Forst- und Ernährungswirtschaft - Intelligente Systeme - Stand der Technik und neue Möglichkeiten. 2016: 97-100

Spatial Learning and Localization in Animals: A Computational Model and Its Implications for Mobile Robots

The ability to acquire a representation of spatial environment and the ability to localize within it are essential for successful navigation in a-priori unknown environments. The hippocampal formation is believed to play a key role in spatial learning and navigation in animals. This paper briefly reviews the relevant neurobiological and cognitive data and their relation to computational models of spatial learning and localization used in mobile robots. It also describes a hippocampal model of spatial learning and navigation and analyzes it using Kalman filter based tools for information fusion from multiple uncertain sources. The resulting model allows a robot to learn a place-based, metric representation of space in a-priori unknown environments and to localize itself in a stochastically optimal manner. The paper also describes an algorithmic implementation of the model and results of several experiments that demonstrate its capabilities

Indexation d'une base de données images : application à la localisation et la cartographie fondées sur des radio-étiquettes et des amers visuels pour la navigation d'un robot en milieu intérieur

Ce mémoire concerne les techniques d'indexation dans des bases d'image, ainsi que les méthodes de localisation en robotique mobile. Il fait le lien entre les travaux en Perception du pôle Robotique et Intelligence Artificielle du LAAS-CNRS, et les recherches sur la fouille de données menées à l'Université de Rabat. Depuis une dizaine d'années, la vision est devenue une source de données sensorielles essentielles sur les robots mobiles: elle fournit en particulier des représentations de l'environnement dans lequel doit se déplacer un robot sous la forme de modèles géométriques ou de modèles fondés sur l'apparence. Concernant la vision, seules les représentations fondées sur l'apparence ont été considérées; elles consistent en une base d'images acquises lors de déplacements effectués de manière supervisée durant une phase d'apprentissage. Le robot se localise en recherchant dans la base d'images, celle qui ressemble le plus à l'image courante: les techniques exploitées pour ce faire sont des méthodes d'indexation, similaires à celles exploitées en fouille de données sur Internet par exemple. Nous proposons une approche fondés sur des points d'intérêt extraits d'images en couleur texturées. Par ailleurs, nous présentons une technique de navigation par RFID (Radio Frequency IDentifier) qui utilise la méthode MonteCarlo, appliquée soit de manière intuitive, soit de manière formelle. Enfin, nous donnons des résultats très préliminaires sur la combinaison d'une perception par capteurs RFID et par capteurs visuels afin d'améliorer la précision de la localisation du robot mobile. ABSTRACT : This document is related both to indexing methods in image data bases and to localization methods used in mobile robotics. It exploits the relationships between research works on Perception made in the Robotics department of LAAS-CNRS in Toulouse, and on Data Mining performed by the LIMAIARF lab at the Rabat University. Computer Vision has become a major source of sensory data on mobile robots for about ten years; it allows to build especially representations of the environment in which a robot has to execute motions, either by geometrical maps or by appearance-based models. Concerning computer vision, only appearance-based representations have been studied; they consist on a data base of images acquired while the robot is moved in an interactive way during a learning step. Robot self-localization is provided by searching in this data base, the image which looks like the one acquired from the current position: this function is performed from indexing or data mining methods, similar to the ones used on Internet. It is proposed an indexing method based on interest points extracted from color and textured images. Moreover, geometrical representations are also considered for an RFID-based navigation method, based on particle filtering, used either in a classical formal way or with a more intuitive approach. Finally, very preliminar results are described on a multi-sensory approach using both Vision and RFID tags in order to improve the accuracy on the robot localizatio

Biologically inspired computational structures and processes for autonomous agents and robots

Recent years have seen a proliferation of intelligent agent applications: from robots for space exploration to software agents for information filtering and electronic commerce on the Internet. Although the scope of these agent applications have blossomed tremendously since the advent of compact, affordable computing (and the recent emergence of the World Wide Web), the design of such agents for specific applications remains a daunting engineering problem;Rather than approach the design of artificial agents from a purely engineering standpoint, this dissertation views animals as biological agents, and considers artificial analogs of biological structures and processes in the design of effective agent behaviors. In particular, it explores behaviors generated by artificial neural structures appropriately shaped by the processes of evolution and spatial learning;The first part of this dissertation deals with the evolution of artificial neural controllers for a box-pushing robot task. We show that evolution discovers high fitness structures using little domain-specific knowledge, even in feedback-impoverished environments. Through a careful analysis of the evolved designs we also show how evolution exploits the environmental constraints and properties to produce designs of superior adaptive value. By modifying the task constraints in controlled ways, we also show the ability of evolution to quickly adapt to these changes and exploit them to obtain significant performance gains. We also use evolution to design the sensory systems of the box-pushing robots, particularly the number, placement, and ranges of their sensors. We find that evolution automatically discards unnecessary sensors retaining only the ones that appear to significantly affect the performance of the robot. This optimization of design across multiple dimensions (performance, number of sensors, size of neural controller, etc.) is implicitly achieved by the evolutionary algorithm without any external pressure (e.g., penalty on the use of more sensors or neurocontroller units). When used in the design of robots with limited battery capacities , evolution produces energy-efficient robot designs that use minimal numbers of components and yet perform reasonably well. The performance as well as the complexity of robot designs increase when the robots have access to a spatial learning mechanism that allows them to learn, remember, and navigate to power sources in the environment;The second part of this dissertation develops a computational characterization of the hippocampal formation which is known to play a significant role in animal spatial learning. The model is based on neuroscientific and behavioral data, and learns place maps based on interactions of sensory and dead-reckoning information streams. Using an estimation mechanism known as Kalman filtering, the model explicitly deals with uncertainties in the two information streams, allowing the robot to effectively learn and localize even in the presence sensing and motion errors. Additionally, the model has mechanisms to handle perceptual aliasing problems (where multiple places in the environment appear sensorily identical), incrementally learn and integrate local place maps, and learn and remember multiple goal locations in the environment. We show a number of properties of this spatial learning model including computational replication of several behavioral experiments performed with rodents. Not only does this model make significant contributions to robot localization, but also offers a number of predictions and suggestions that can be validated (or refuted) through systematic neurobiological and behavioral experiments with animals

Precision autonomous underwater navigation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2003.Includes bibliographical references (p. 175-185).Deep-sea archaeology, an emerging application of autonomous underwater vehicle (AUV) technology, requires precise navigation and guidance. As science requirements and engineering capabilities converge, navigating in the sensor-limited ocean remains a fundamental challenge. Despite the logistical cost, the standards of archaeological survey necessitate using fixed acoustic transponders - an instrumented navigation environment. This thesis focuses on the problems particular to operating precisely within such an environment by developing a design method and a navigation algorithm. Responsible documentation, through remote sensing images, distinguishes archaeology from salvage, and fine-resolution imaging demands precision navigation. This thesis presents a design process for making component and algorithm level tradeoffs to achieve system-level performance satisfying the archaeological standard. A specification connects the functional requirements of archaeological survey with the design parameters of precision navigation. Tools based on estimation fundamentals - the Cram6r-Rao lower bound and the extended Kalman filter - predict the system-level precision of candidate designs. Non-dimensional performance metrics generalize the analysis results. Analyzing a variety of factors and levels articulates the key tradeoffs: sensor selection, acoustic beacon configuration, algorithm selection, etc. The abstract analysis is made concrete by designing a survey and navigation system for an expedition to image the USS Monitor. Hypothesis grid (Hgrid) is both a representation of the sensed environment and an algorithm for building the representation. Range observations measuring the line-of-sight distance between two acoustic transducers are subject to multipath errors and spurious returns.The quality of this measurement is dependent on the location of the estimator. Hgrids characterize the measurement quality by generating a priori association probabilities - the belief that subsequent measurements will correspond to the direct-path, a multipath, or an outlier - as a function of the estimated location. The algorithm has three main components: the mixed-density sensor model using Gaussian and uniform probability distributions, the measurement classification and multipath model identification using expectation-maximization (EM), and the grid-based spatial representation. Application to data from an autonomous benthic explorer (ABE) dive illustrates the algorithm and shows the feasibility of the approach.by Brian Steven Bingham.Ph.D

Probabilistic environment perception for driver assistance systems

Viele aktuelle Fahrerassistenzsysteme wie beispielsweise die adaptive Geschwindigkeitsregelung, Spurwechselassistenten und Systeme zur Anhaltewegverkürzung sind auf eine verlässliche Detektion anderer Verkehrsteilnehmer und Hindernisse angewiesen. Zukünftige Assistenzsysteme wie beispielsweise Systeme für das Automatische Fahren erhöhen diese Zuverlässigkeitsanforderung weiter. Die Dissertation befasst sich mit der statistisch genauen Bewertung von Objekthypothesen innerhalb einer Sensordatenfusion, welche aus Messdaten gewonnen wurden. Für jede Hypothese wird eine Wahrscheinlichkeit bestimmt, welche angibt, ob diese vom Fahrerassistenzsystem berücksichtigt werden muss. Hierbei werden widersprüchliche Messdaten systematisch in probabilistischen Modellen aufgelöst, wobei zur Approximation der Wahrscheinlichkeitsdichtefunktion geeignete Modelle aus dem Bereich des Maschinellen Lernens eingesetzt werden. Als Ergebnis erhält man einen Schätzer, der eine präzise Relevanzwahrscheinlichkeit für beliebige Objekthypothesen erzeugt, sodass das Fahrerassistenzsystem frühzeitig und angemessen auf ein aktuelles Umfeld reagieren kann. Neben dem Objekthypothesenmodell ist als zweiter Typ von Umfeldmodellen das Belegungsgitter verbreitet, welches den Raum um das Fahrzeug in Zellen diskretisiert. Die Messdaten werden mit den jeweiligen örtlich zugehörigen Zellen assoziiert und deren Zustand wird aktualisiert. Als Ergebnis erhält man eine Menge von Zellen mit unterschiedlichen Zuständen, die beispielsweise die Überfahrbarkeit repräsentieren. Die Dissertation entwickelt formale Eigenschaften, die Fusions- und Abfragealgorithmen aufweisen müssen, um eine statistisch belastbare Aussage über die Befahrbarkeit eines aus vielen Zellen bestehenden Korridors liefern zu können. Zusätzlich werden exemplarische Algorithmen entwickelt, die diese Eigenschaften berücksichtigen und somit eine präzisere Schätzung als bekannte Ansätze erlauben.Many of today's driver assistance systems, like adaptive cruise control, lane change assistant or collision avoidance and mitigation systems require a reliable perception of other traffic participants and obstacles. Future driver assistance systems like automatic driving will further increase the requirement of a reliable environment perception. This thesis deals with the validation of object hypotheses that are generated on the base of measurements inside a sensor data fusion software. A statistically accurate probability of each object hypothesis is generated, which indicates if it should be considered by the driver assistance system. Contradictory data will be resolved systematically using probabilistic models. To approximate the underlying probabilistic density function, proper Machine Learning algorithms are used. As a result, an estimator can be presented that generates a accurate relevance probability for every object hypothesis. Driver assistance systems can now react more early and more adequately to the current environment. Beside the object model, a second type of environment model is common: The occupancy grid discretises the space around the vehicle into cells, in which each of them contains a cell state. These cell states are updated with measurements that can be associated with the cell's position. As a result, a set of cell states is generated that may represent, for instance, their trafficability. To provide a trafficability estimation of a corridor consisting of many cells, formal mathematical standards are developed. These standards must be considered from both fusion and query algorithms to perform a statistically correct estimation. Additionally, exemplary algorithms with these features are developed which can do a more accurate estimation than common approaches