Contributions to autonomous robust navigation of mobile robots in industrial applications

151 p.Un aspecto en el que las plataformas móviles actuales se quedan atrás en comparación con el punto que se ha alcanzado ya en la industria es la precisión. La cuarta revolución industrial trajo consigo la implantación de maquinaria en la mayor parte de procesos industriales, y una fortaleza de estos es su repetitividad. Los robots móviles autónomos, que son los que ofrecen una mayor flexibilidad, carecen de esta capacidad, principalmente debido al ruido inherente a las lecturas ofrecidas por los sensores y al dinamismo existente en la mayoría de entornos. Por este motivo, gran parte de este trabajo se centra en cuantificar el error cometido por los principales métodos de mapeado y localización de robots móviles,ofreciendo distintas alternativas para la mejora del posicionamiento.Asimismo, las principales fuentes de información con las que los robots móviles son capaces de realizarlas funciones descritas son los sensores exteroceptivos, los cuales miden el entorno y no tanto el estado del propio robot. Por esta misma razón, algunos métodos son muy dependientes del escenario en el que se han desarrollado, y no obtienen los mismos resultados cuando este varía. La mayoría de plataformas móviles generan un mapa que representa el entorno que les rodea, y fundamentan en este muchos de sus cálculos para realizar acciones como navegar. Dicha generación es un proceso que requiere de intervención humana en la mayoría de casos y que tiene una gran repercusión en el posterior funcionamiento del robot. En la última parte del presente trabajo, se propone un método que pretende optimizar este paso para así generar un modelo más rico del entorno sin requerir de tiempo adicional para ello

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 /

Local Accuracy and Global Consistency for Efficient SLAM

This thesis is concerned with the problem of Simultaneous Localisation and Mapping (SLAM) using visual data only. Given the video stream of a moving camera, we wish to estimate the structure of the environment and the motion of the device most accurately and in real-time. Two effective approaches were presented in the past. Filtering methods marginalise out past poses and summarise the information gained over time with a probability distribution. Keyframe methods rely on the optimisation approach of bundle adjustment, but computationally must select only a small number of past frames to process. We perform a rigorous comparison between the two approaches for visual SLAM. Especially, we show that accuracy comes from a large number of points, while the number of intermediate frames only has a minor impact. We conclude that keyframe bundle adjustment is superior to ltering due to a smaller computational cost. Based on these experimental results, we develop an efficient framework for large-scale visual SLAM using the keyframe strategy. We demonstrate that SLAM using a single camera does not only drift in rotation and translation, but also in scale. In particular, we perform large-scale loop closure correction using a novel variant of pose-graph optimisation which also takes scale drift into account. Starting from this two stage approach which tackles local motion estimation and loop closures separately, we develop a unified framework for real-time visual SLAM. By employing a novel double window scheme, we present a constant-time approach which enables the local accuracy of bundle adjustment while ensuring global consistency. Furthermore, we suggest a new scheme for local registration using metric loop closures and present several improvements for the visual front-end of SLAM. Our contributions are evaluated exhaustively on a number of synthetic experiments and real-image data-set from single cameras and range imaging devices

Local Accuracy and Global Consistency for Efficient SLAM

This thesis is concerned with the problem of Simultaneous Localisation and Mapping (SLAM) using visual data only. Given the video stream of a moving camera, we wish to estimate the structure of the environment and the motion of the device most accurately and in real-time. Two effective approaches were presented in the past. Filtering methods marginalise out past poses and summarise the information gained over time with a probability distribution. Keyframe methods rely on the optimisation approach of bundle adjustment, but computationally must select only a small number of past frames to process. We perform a rigorous comparison between the two approaches for visual SLAM. Especially, we show that accuracy comes from a large number of points, while the number of intermediate frames only has a minor impact. We conclude that keyframe bundle adjustment is superior to ltering due to a smaller computational cost. Based on these experimental results, we develop an efficient framework for large-scale visual SLAM using the keyframe strategy. We demonstrate that SLAM using a single camera does not only drift in rotation and translation, but also in scale. In particular, we perform large-scale loop closure correction using a novel variant of pose-graph optimisation which also takes scale drift into account. Starting from this two stage approach which tackles local motion estimation and loop closures separately, we develop a unified framework for real-time visual SLAM. By employing a novel double window scheme, we present a constant-time approach which enables the local accuracy of bundle adjustment while ensuring global consistency. Furthermore, we suggest a new scheme for local registration using metric loop closures and present several improvements for the visual front-end of SLAM. Our contributions are evaluated exhaustively on a number of synthetic experiments and real-image data-set from single cameras and range imaging devices

EVAQ: Person-Specific Evacuation Simulation for Large Crowd Egress Analysis

Timely crowd evacuation in life-threatening situations such as fire emergency or terrorist attack is a significant concern for authorities and first responders. An individual’s fate in this kind of situation is highly dependent on a host of factors, especially (i) agent dynamics: how the individual selects and executes an egress strategy, (ii) hazard dynamics: how hazards propagate (e.g., fire and smoke spread, lone wolf attacker moves) and impair the surrounding environment with time, (iii) intervention dynamics: how first responders intervene (e.g., firefighters spread repellents) to recover environment. This thesis presents EVAQ, a simulation modeling framework for evaluating the impact of these factors on the likelihood of survival in an emergency evacuation. The framework captures the effect of personal traits and physical habitat parameters on occupants’ decision-making. In particular, personal (i.e., age, gender, disability) and interpersonal (i.e., agent-agent interactions) attributes, as well as an individual’s situational awareness are parameterized in a deteriorating environment considering different exit layouts and physical constraints. Further, the framework supports a variety of hazard propagation schemes (e.g., fire spreading in a given direction, lone wolf attacker targeting individuals), and intervene schemes (e.g., firefighters spreading repellents, police catch the attacker) to support a wide range of emergency evacuation scenarios. The application of EVAQ to crowd egress planning in an airport terminal and a shopping mall in the fire emergency is presented in this thesis, and results are discussed. Result shows that the likelihood of survival decreases with a decrease in availability of the nearest exits and a resulting increase in congestions in the environment. Also, it is observed that the incorporation of group behavior increases the likelihood of survival for children, as well as elderly and disabled people. In addition, several verifications and validation tests are performed to assess the reliability and integrity of EVAQ in comparison with existing evacuation modeling tools. As personalized sensing and information delivery platforms are becoming more ubiquitous, findings of this work are ultimately sought to assist in developing and executing more robust and adaptive emergency mapping and evacuation plans, ultimately aimed at promoting people’s lives and wellbeing

Exploration autonome et efficiente de chantiers miniers souterrains inconnus avec un drone filaire

Abstract: Underground mining stopes are often mapped using a sensor located at the end of a pole that the operator introduces into the stope from a secure area. The sensor emits laser beams that provide the distance to a detected wall, thus creating a 3D map. This produces shadow zones and a low point density on the distant walls. To address these challenges, a research team from the Université de Sherbrooke is designing a tethered drone equipped with a rotating LiDAR for this mission, thus benefiting from several points of view. The wired transmission allows for unlimited flight time, shared computing, and real-time communication. For compatibility with the movement of the drone after tether entanglements, the excess length is integrated into an onboard spool, contributing to the drone payload. During manual piloting, the human factor causes problems in the perception and comprehension of a virtual 3D environment, as well as the execution of an optimal mission. This thesis focuses on autonomous navigation in two aspects: path planning and exploration. The system must compute a trajectory that maps the entire environment, minimizing the mission time and respecting the maximum onboard tether length. Path planning using a Rapidly-exploring Random Tree (RRT) quickly finds a feasible path, but the optimization is computationally expensive and the performance is variable and unpredictable. Exploration by the frontier method is representative of the space to be explored and the path can be optimized by solving a Traveling Salesman Problem (TSP) but existing techniques for a tethered drone only consider the 2D case and do not optimize the global path. To meet these challenges, this thesis presents two new algorithms. The first one, RRT-Rope, produces an equal or shorter path than existing algorithms in a significantly shorter computation time, up to 70% faster than the next best algorithm in a representative environment. A modified version of RRT-connect computes a feasible path, shortened with a deterministic technique that takes advantage of previously added intermediate nodes. The second algorithm, TAPE, is the first 3D cavity exploration method that focuses on minimizing mission time and unwound tether length. On average, the overall path is 4% longer than the method that solves the TSP, but the tether remains under the allowed length in 100% of the simulated cases, compared to 53% with the initial method. The approach uses a 2-level hierarchical architecture: global planning solves a TSP after frontier extraction, and local planning minimizes the path cost and tether length via a decision function. The integration of these two tools in the NetherDrone produces an intelligent system for autonomous exploration, with semi-autonomous features for operator interaction. This work opens the door to new navigation approaches in the field of inspection, mapping, and Search and Rescue missions.La cartographie des chantiers miniers souterrains est souvent réalisée à l’aide d’un capteur situé au bout d’une perche que l’opérateur introduit dans le chantier, depuis une zone sécurisée. Le capteur émet des faisceaux laser qui fournissent la distance à un mur détecté, créant ainsi une carte en 3D. Ceci produit des zones d’ombres et une faible densité de points sur les parois éloignées. Pour relever ces défis, une équipe de recherche de l’Université de Sherbrooke conçoit un drone filaire équipé d’un LiDAR rotatif pour cette mission, bénéficiant ainsi de plusieurs points de vue. La transmission filaire permet un temps de vol illimité, un partage de calcul et une communication en temps réel. Pour une compatibilité avec le mouvement du drone lors des coincements du fil, la longueur excédante est intégrée dans une bobine embarquée, qui contribue à la charge utile du drone. Lors d’un pilotage manuel, le facteur humain entraîne des problèmes de perception et compréhension d’un environnement 3D virtuel, et d’exécution d’une mission optimale. Cette thèse se concentre sur la navigation autonome sous deux aspects : la planification de trajectoire et l’exploration. Le système doit calculer une trajectoire qui cartographie l’environnement complet, en minimisant le temps de mission et en respectant la longueur maximale de fil embarquée. La planification de trajectoire à l’aide d’un Rapidly-exploring Random Tree (RRT) trouve rapidement un chemin réalisable, mais l’optimisation est coûteuse en calcul et la performance est variable et imprévisible. L’exploration par la méthode des frontières est représentative de l’espace à explorer et le chemin peut être optimisé en résolvant un Traveling Salesman Problem (TSP), mais les techniques existantes pour un drone filaire ne considèrent que le cas 2D et n’optimisent pas le chemin global. Pour relever ces défis, cette thèse présente deux nouveaux algorithmes. Le premier, RRT-Rope, produit un chemin égal ou plus court que les algorithmes existants en un temps de calcul jusqu’à 70% plus court que le deuxième meilleur algorithme dans un environnement représentatif. Une version modifiée de RRT-connect calcule un chemin réalisable, raccourci avec une technique déterministe qui tire profit des noeuds intermédiaires préalablement ajoutés. Le deuxième algorithme, TAPE, est la première méthode d’exploration de cavités en 3D qui minimise le temps de mission et la longueur du fil déroulé. En moyenne, le trajet global est 4% plus long que la méthode qui résout le TSP, mais le fil reste sous la longueur autorisée dans 100% des cas simulés, contre 53% avec la méthode initiale. L’approche utilise une architecture hiérarchique à 2 niveaux : la planification globale résout un TSP après extraction des frontières, et la planification locale minimise le coût du chemin et la longueur de fil via une fonction de décision. L’intégration de ces deux outils dans le NetherDrone produit un système intelligent pour l’exploration autonome, doté de fonctionnalités semi-autonomes pour une interaction avec l’opérateur. Les travaux réalisés ouvrent la porte à de nouvelles approches de navigation dans le domaine des missions d’inspection, de cartographie et de recherche et sauvetage

Robot Area Coverage Path Planning in Aquatic Environments

This thesis is motivated by real world problems faced in aquatic environments. It addresses the problem of area coverage path planning with robots - the problem of moving an end-effector of a robot over all available space while avoiding existing obstacles. The problem is considered first in a 2D space with a single robot for specific environmental monitoring operations, and then with multi-robot systems — a known NP-complete problem. Next we tackle the coverage problem in 3D space - a step towards underwater mapping of shipwrecks or monitoring of coral reefs. The first part of this thesis leverages human expertise in river exploration and data collection strategies to automate and optimize environmental monitoring and surveying operations using autonomous surface vehicles (ASVs). In particular, four deterministic algorithms for both partial and complete coverage of a river segment are proposed, providing varying path length, coverage density, and turning patterns. These strategies result in increases in accuracy and efficiency compared to manual approaches taken by scientists. The proposed methods were extensively tested in simulation using maps of real rivers of different shapes and sizes. In addition, to verify their performance in real world operations, the ASVs were deployed successfully on several parts of the Congaree River in South Carolina, USA, resulting in a total of more than 35km of coverage trajectories in the field. In large scale coverage operations, such as marine exploration or aerial monitoring, single robot approaches are not ideal. Not only the coverage might take too long, but the robot might run out of battery charge before completing the task. In such scenarios, multi-robot approaches are preferable. Furthermore, several real world vehicles In large scale coverage operations, such as marine exploration or aerial monitoring, single robot approaches are not ideal. Not only the coverage might take too long, but the robot might run out of battery charge before completing the task. In such scenarios, multi-robot approaches are preferable. Furthermore, several real world vehicles are non-holonomic, but can be modeled using Dubins vehicle kinematics. The second part of this thesis focuses on environmental monitoring of aquatic domains using a team of Autonomous Surface Vehicles (ASVs) that have Dubins vehicle constraints. It is worth noting that both multi-robot coverage and Dubins vehicle coverage are NPcomplete problems. As such, we present two heuristic methods based on a variant of the traveling salesman problem—k-TSP—formulation and clustering algorithms that efficiently solve the problem. The proposed methods are tested both in simulations and with a team of ASVs operating on a 40 000m2 lake area to assess their ability to scale to the real world. Finally, in the third part, a step towards solving the coverage path planning problem in a 3D environment for surveying underwater structures, employing vision-only navigation strategies, is presented. Given the challenging conditions of the underwater domain, it is very complicated to obtain accurate state estimates reliably. Consequently, it is a great challenge to extend known path planning or coverage techniques developed for aerial or ground robot controls. In this work we are investigating a navigation strategy utilizing only vision to assist in covering a complex underwater structure. We propose to use a navigation strategy akin to what a human diver will execute when circumnavigating around a region of interest, in particular when collecting data from a shipwreck. The focus of this work is a step towards enabling the autonomous operation of lightweight agile robots near underwater wrecks in order to collect data for creating photo-realistic maps and volumetric 3D models while at the same time avoiding collisions. The proposed method uses convolutional neural networks (CNNs) to learn the control commands based on the visual input. We have demonstrated the feasibility of using a system based only on vision to learn specific strategies of navigation, with 80% accuracy on the prediction of control command changes. Experimental results and a detailed overview of the proposed method are discussed

Vertex unique labelled subgraph mining

This thesis proposes the novel concept of Vertex Unique Labelled Subgraph (VULS) mining with respect to the field of graph-based knowledge discovery (or graph mining). The objective of the research is to investigate the benefits that the concept of VULS can offer in the context of vertex classification. A VULS is a subgraph with a particular structure and edge labelling that has a unique vertex labelling associated with it within a given (set of) host graph(s). VULS can describe highly discriminative and significant local geometries each with a particular associated vertex label pattern. This knowledge can then be used to predict vertex labels in ``unseen" graphs (graphs with edge labels, but without vertex labels). Thus this research is directed at identifying (mining) VULS, of various forms, that ``best" serve to both capture effectively graph information, while at the same time allowing for the generation of effective vertex label predictors (classifiers). To this end, four VULS classifiers are proposed, directed at mining four different kinds of VULS: (i) complete, (ii) minimal, (iii) frequent and (iv) minimal frequent. The thesis describes and discusses each of these in detail including, in each case, the theoretical definition and algorithms with respect to VULS identification and prediction. A full evaluation of each of the VULS categories is also presented. VULS has wide applicability in areas where the domain of interest can be represented in the form of some sort of a graph. The evaluation was primarily directed at predicting a form of deformation, known as springback, that occurs in the Asymmetric Incremental Sheet Forming (AISF) manufacturing process. For the evaluation two flat-topped, square-based, pyramid shapes were used. Each pyramid had been manufactured twice using Steel and twice using Titanium. The utilisation of VULS was also explored by applying the VULS concept to the field of satellite image interpretation. Satellite data describing two villages located in a rural part of the Ethiopian hinterland were used for this purpose. In each case the ground surface was represented in a similar manner to the way that AISF sheet metal surfaces were represented, with the $z$ dimension describing the grey scale value. The idea here was to predict vertex labels describing ground type. As will become apparent, from the work presented in this thesis, the VULS concept is well suited to the task of 3D surface classification with respect to AISF and satellite imagery. The thesis demonstrates that the use of frequent VULS (rather than the other forms of VULS considered) produces more efficient results in the AISF sheet metal forming application domain, whilst the use of minimal VULS provided promising results in the context of the satellite image interpretation domain. The reported evaluation also indicates that a sound foundation has been established for future work on more general VULS based vertex classification

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine