Topological Navigation of Simulated Robots using Occupancy Grid

Formerly I presented a metric navigation method in the Webots mobile robot simulator. The navigating Khepera-like robot builds an occupancy grid of the environment and explores the square-shaped room around with a value iteration algorithm. Now I created a topological navigation procedure based on the occupancy grid process. The extension by a skeletonization algorithm results a graph of important places and the connecting routes among them. I also show the significant time profit gained during the process

DM-PhyClus: A Bayesian phylogenetic algorithm for infectious disease transmission cluster inference

Background. Conventional phylogenetic clustering approaches rely on arbitrary cutpoints applied a posteriori to phylogenetic estimates. Although in practice, Bayesian and bootstrap-based clustering tend to lead to similar estimates, they often produce conflicting measures of confidence in clusters. The current study proposes a new Bayesian phylogenetic clustering algorithm, which we refer to as DM-PhyClus, that identifies sets of sequences resulting from quick transmission chains, thus yielding easily-interpretable clusters, without using any ad hoc distance or confidence requirement. Results. Simulations reveal that DM-PhyClus can outperform conventional clustering methods, as well as the Gap procedure, a pure distance-based algorithm, in terms of mean cluster recovery. We apply DM-PhyClus to a sample of real HIV-1 sequences, producing a set of clusters whose inference is in line with the conclusions of a previous thorough analysis. Conclusions. DM-PhyClus, by eliminating the need for cutpoints and producing sensible inference for cluster configurations, can facilitate transmission cluster detection. Future efforts to reduce incidence of infectious diseases, like HIV-1, will need reliable estimates of transmission clusters. It follows that algorithms like DM-PhyClus could serve to better inform public health strategies

Sensory processing and world modeling for an active ranging device

In this project, we studied world modeling and sensory processing for laser range data. World Model data representation and operation were defined. Sensory processing algorithms for point processing and linear feature detection were designed and implemented. The interface between world modeling and sensory processing in the Servo and Primitive levels was investigated and implemented. In the primitive level, linear features detectors for edges were also implemented, analyzed and compared. The existing world model representations is surveyed. Also presented is the design and implementation of the Y-frame model, a hierarchical world model. The interfaces between the world model module and the sensory processing module are discussed as well as the linear feature detectors that were designed and implemented

Exploiting graph structure in Active SLAM

Aplicando análisis provenientes de la teoría de grafos, la teoría espectral de grafos, la exploración de grafos en línea, generamos un sistema de SLAM activo que incluye la planificación de rutas bajo incertidumbre, extracción de grafos topológicos de entornos y SLAM activo \'optimo.En la planificación de trayectorias bajo incertidumbre, incluimos el análisis de la probabilidad de asociación correcta de datos. Reconociendo la naturaleza estocástica de la incertidumbre, demostramos que planificar para minimizar su valor esperado es más fiable que los actuales algoritmos de planificación de trayectorias con incertidumbre.Considerando el entorno como un conjunto de regiones convexas conectadas podemos tratar la exploración robótica como una exploración de grafos en línea. Se garantiza una cobertura total si el robot visita cada región. La mayoría de los métodos para segmentar el entorno están basados en píxeles y no garantizan que las regiones resultantes sean convexas, además pocos son algoritmos incrementales. En base a esto, modificamos un algoritmo basado en contornos en el que el entorno se representa como un conjunto de polígonos que debe segmentarse en un conjunto de polígonos pseudo convexos. El resultado es un algoritmo de segmentación que produjo regiones pseudo-convexas, robustas al ruido, estables y que obtienen un gran rendimiento en los conjuntos de datos de pruebas.La calidad de un algoritmo se puede medir en términos de cuan cercano al óptimo está su rendimiento. Con esta motivación definimos la esencia de la tarea de exploración en SLAM activo donde las únicas variables son la distancia recorrida y la calidad de la reconstrucción. Restringiendo el dominio al grafo que representa el entorno y probando la relación entre la matriz asociada a la exploración y la asociada al grafo subyacente, podemos calcular la ruta de exploración óptima.A diferencia de la mayoría de la literatura en SLAM activo, proponemos que la heurística para la exploración de grafos consiste en atravesar cada arco una vez. Demostramos que el tipo de grafos resultantes tiene un gran rendimiento con respecto a la trayectoria \'optima, con resultados superiores al 97 \% del \'optimo en algunas medidas de calidad.El algoritmo de SLAM activo TIGRE integra el algoritmo de extracción de grafos propuesto con nuestra versión del algoritmo de exploración incremental que atraviesa cada arco una vez. Nuestro algoritmo se basa en una modificación del algoritmo clásico de Tarry para la búsqueda en laberintos que logra el l\'imite inferior en la aproximación para un algoritmo incremental. Probamos nuestro sistema incremental en un escenario de exploración típico y demostramos que logra un rendimiento similar a los métodos fuera de línea y también demostramos que incluso el método \'optimo que visita todos los nodos calculado fuera de línea tiene un peor rendimiento que el nuestro.<br /

A two-step approach Bayesian network model selection

It is often desirable to show relationships between unstructured, potentially related data elements, or features, composing a knowledge database (KD). By understanding the interaction between these elements, we may gain insight into the underlying process from which the KD is derived, and as a result, we can often model the process. Bayesian Belief Networks (BBN) in particular, are adept at modeling knowledge databases for two reasons. The first is that BBNs give a structural representation of data elements through a directed acyclic graph (DAG). This ability may make BBNs invaluable in areas such as data mining, where statistical relationships between the features of a traditional database are not apparent. An accurate BBN will clearly show features exerting influences on each other. The second strength of the BBN model is its ability to encode conditional expectations between knowledge database features. This ability facilitates using BBNs as inference engines. Given a set of instantiated elements, BBNs allow us to derive the most statistically likely instantiation of states for elements whose state is unknown. These qualities lend themselves to BBNs being proficient in applications ranging from computer vision to risk-assessment. In this thesis, two frameworks for BBN structure learning, or model selection, will be compared. The first is the asymptotically correct structure learning algorithm which shows efficient search space exploration characteristics. The second takes permutations of global structures in an elitist elimination heuristic search and shows precise search space exploitation characteristics. Comparisons between techniques will be presented, paying particular attention to computational complexity versus model precision. In the elitist elimination technique, comparisons between the Minimum Description Length (MDL) scoring heuristic and the Database probability given Model (DGM) scoring heuristic, will be provided. A comparison between naïve and non-naïve structure learning will be made along with an analysis of the infeasibility of naïve BBN model selection. Finally, an efficient and precise algorithm tor learning BBNs, which utilizes both frameworks, will be proposed

Efficient mobile robot path planning by Voronoi-based heuristic

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Uncovering representations of sleep-associated hippocampal ensemble spike activity

Pyramidal neurons in the rodent hippocampus exhibit spatial tuning during spatial navigation, and they are reactivated in specific temporal order during sharp-wave ripples observed in quiet wakefulness or slow wave sleep. However, analyzing representations of sleep-associated hippocampal ensemble spike activity remains a great challenge. In contrast to wake, during sleep there is a complete absence of animal behavior, and the ensemble spike activity is sparse (low occurrence) and fragmental in time. To examine important issues encountered in sleep data analysis, we constructed synthetic sleep-like hippocampal spike data (short epochs, sparse and sporadic firing, compressed timescale) for detailed investigations. Based upon two Bayesian population-decoding methods (one receptive field-based, and the other not), we systematically investigated their representation power and detection reliability. Notably, the receptive-field-free decoding method was found to be well-tuned for hippocampal ensemble spike data in slow wave sleep (SWS), even in the absence of prior behavioral measure or ground truth. Our results showed that in addition to the sample length, bin size, and firing rate, number of active hippocampal pyramidal neurons are critical for reliable representation of the space as well as for detection of spatiotemporal reactivated patterns in SWS or quiet wakefulness.Collaborative Research in Computational Neuroscience (Award IIS-1307645)United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N00014-10-1-0936)National Institutes of Health (U.S.) (Grant TR01-GM10498

Methodology and Algorithms for Pedestrian Network Construction

With the advanced capabilities of mobile devices and the success of car navigation systems, interest in pedestrian navigation systems is on the rise. A critical component of any navigation system is a map database which represents a network (e.g., road networks in car navigation systems) and supports key functionality such as map display, geocoding, and routing. Road networks, mainly due to the popularity of car navigation systems, are well defined and publicly available. However, in pedestrian navigation systems, as well as other applications including urban planning and physical activities studies, road networks do not adequately represent the paths that pedestrians usually travel. Currently, there are no techniques to automatically construct pedestrian networks, impeding research and development of applications requiring pedestrian data. This coupled with the increased demand for pedestrian networks is the prime motivation for this dissertation which is focused on development of a methodology and algorithms that can construct pedestrian networks automatically. A methodology, which involves three independent approaches, network buffering (using existing road networks), collaborative mapping (using GPS traces collected by volunteers), and image processing (using high-resolution satellite and laser imageries) was developed. Experiments were conducted to evaluate the pedestrian networks constructed by these approaches with a pedestrian network baseline as a ground truth. The results of the experiments indicate that these three approaches, while differing in complexity and outcome, are viable for automatically constructing pedestrian networks

Hybrid, metric - topological, mobile robot navigation

This thesis presents a recent research on the problem of environmental modeling for both localization and map building for wheel-based, differential driven, fully autonomous and self-contained mobile robots. The robots behave in an indoor office environment. They have a multi-sensor setup where the encoders are used for odometry and two exteroperceptive sensors, a 360° laser scanner and a monocular vision system, are employed to perceive the surrounding. The whole approach is feature based meaning that instead of directly using the raw data from the sensor features are firstly extracted. This allows the filtering of noise from the sensors and permits taking account of the dynamics in the environment. Furthermore, a properly chosen feature extraction has the characteristic of better isolating informative patterns. When describing these features care has to be taken that the uncertainty from the measurements is taken into account. The representation of the environment is crucial for mobile robot navigation. The model defines which perception capabilities are required and also which navigation technique is allowed to be used. The presented environmental model is both metric and topological. By coherently combining the two paradigms the advantages of both methods are added in order to face the drawbacks of a single approach. The capabilities of the hybrid approach are exploited to model an indoor office environment where metric information is used locally in structures (rooms, offices), which are naturally defined by the environment itself while the topology of the whole environment is resumed separately thus avoiding the need of global metric consistency. The hybrid model permits the use of two different and complementary approaches for localization, map building and planning. This combination permits the grouping of all the characteristics which enables the following goals to be met: Precision, robustness and practicability. Metric approaches are, per definition, precise. The use of an Extended Kalman Filter (EKF) permits to have a precision which is just bounded by the quality of the sensor data. Topological approaches can easily handle large environments because they do not heavily rely on dead reckoning. Global consistency can, therefore, be maintained for large environments. Consistent mapping, which handle large environments, is achieved by choosing a topological localization approach, based on a Partially Observable Markov Decision Process (POMDP), which is extended to simultaneous localization and map building. The theory can be mathematically proven by making some assumptions. However, as stated during the whole work, at the end the robot itself has to show how good the theory is when used in the real world. For this extensive experimentation for a total of more than 9 km is performed with fully autonomous self-contained robots. These experiments are then carefully analyzed. With the metric approach precision with error bounds of about 1 cm and less than 1 degree is further confirmed by ground truth measurements with a mean error of less than 1 cm. The topological approach is successfully tested by simultaneous localization and map building where the automatically created maps turned out to work better than the a priori maps. Relocation and closing the loop are also successfully tested

Advances towards behaviour-based indoor robotic exploration

215 p.The main contributions of this research work remain in object recognition by computer vision, by one side, and in robot localisation and mapping by the other. The first contribution area of the research address object recognition in mobile robots. In this area, door handle recognition is of great importance, as it help the robot to identify doors in places where the camera is not able to view the whole door. In this research, a new two step algorithm is presented based on feature extraction that aimed at improving the extracted features to reduce the superfluous keypoints to be compared at the same time that it increased its efficiency by improving accuracy and reducing the computational time. Opposite to segmentation based paradigms, the feature extraction based two-step method can easily be generalized to other types of handles or even more, to other type of objects such as road signals. Experiments have shown very good accuracy when tested in real environments with different kind of door handles. With respect to the second contribution, a new technique to construct a topological map during the exploration phase a robot would perform on an unseen office-like environment is presented. Firstly a preliminary approach proposed to merge the Markovian localisation in a distributed system, which requires low storage and computational resources and is adequate to be applied in dynamic environments. In the same area, a second contribution to terrain inspection level behaviour based navigation concerned to the development of an automatic mapping method for acquiring the procedural topological map. The new approach is based on a typicality test called INCA to perform the so called loop-closing action. The method was integrated in a behaviour-based control architecture and tested in both, simulated and real robot/environment system. The developed system proved to be useful also for localisation purpose