Distributed consensus in multi-robot systems with visual perception

La idea de equipos de robots actuando con autonomía y de manera cooperativa está cada día más cerca de convertirse en realidad. Los sistemas multi robot pueden ejecutar tareas de gran complejidad con mayor robustez y en menos tiempo que un robot trabajando solo. Por otra parte, la coordinación de un equipo de robots introduce complicaciones que los ingenieros encargados de diseñar estos sistemas deben afrontar. Conseguir que la percepción del entorno sea consistente en todos los robots es uno de los aspectos más importantes requeridos en cualquier tarea cooperativa, lo que implica que las observaciones de cada robot del equipo deben ser transmitidas a todos los otros miembros. Cuando dos o más robots poseen información común del entorno, el equipo debe alcanzar un consenso usando toda la información disponible. Esto se debe hacer considerando las limitaciones de cada robot, teniendo en cuenta que no todos los robots se pueden comunicar unos con otros. Con este objetivo, se aborda la tarea de diseñar algoritmos distribuidos que consigan que un equipo de robots llegue a un consenso acerca de la información percibida por todos los miembros. Específicamente, nos centramos en resolver este problema cuando los robots usan la visión como sensor para percibir el entorno. Las cámaras convencionales son muy útiles a la hora de ejecutar tareas como la navegación y la construcción de mapas, esenciales en el ámbito de la robótica, gracias a la gran cantidad de información que contiene cada imagen. Sin embargo, el uso de estos sensores en un marco distribuido introduce una gran cantidad de complicaciones adicionales que deben ser abordadas si se quiere cumplir el objetivo propuesto. En esta Tesis presentamos un estudio profundo de los algoritmos distribuidos de consenso y cómo estos pueden ser usados por un equipo de robots equipados con cámaras convencionales, resolviendo los aspectos más importantes relacionados con el uso de estos sensores. En la primera parte de la Tesis nos centramos en encontrar correspondencias globales entre las observaciones de todos los robots. De esta manera, los robots son capaces de detectar que observaciones deben ser combinadas para el cálculo del consenso. También lidiamos con el problema de la robustez y la detección distribuida de espurios durante el cálculo del consenso. Para contrarrestar el incremento del tamaño de los mensajes intercambiados por los robots en las etapas anteriores, usamos las propiedades de los polinomios de Chebyshev, reduciendo el número de iteraciones que se requieren para alcanzar el consenso. En la segunda parte de la Tesis, centramos nuestra atención en los problemas de crear un mapa y controlar el movimiento del equipo de robots. Presentamos soluciones para alcanzar un consenso en estos escenarios mediante el uso de técnicas de visión por computador ampliamente conocidas. El uso de algoritmos de estructura y movimiento nos permite obviar restricciones tales como que los robots tengan que observarse unos a otros directamente durante el control o la necesidad de especificar un marco de referencia común. Adicionalmente, nuestros algoritmos tienen un comportamiento robusto cuando la calibración de las cámaras no se conoce. Finalmente, la evaluación de las propuestas se realiza utilizando un data set de un entorno urbano y robots reales con restricciones de movimiento no holónomas. Todos los algoritmos que se presentan en esta Tesis han sido diseñados para ser ejecutados de manera distribuida. En la Tesis demostramos de manera teórica las principales propiedades de los algoritmos que se proponen y evaluamos la calidad de los mismos con datos simulados e imágenes reales. En resumen, las principales contribuciones de esta Tesis son: • Un conjunto de algoritmos distribuidos que permiten a un equipo de robots equipados con cámaras convencionales alcanzar un consenso acerca de la información que perciben. En particular, proponemos tres algoritmos distribuidos con el objetivo de resolver los problemas de encontrar correspondencias globales entre la información de todos los robots, detectar y descartar información espuria, y reducir el número de veces que los robots tienen que comunicarse entre ellos antes de alcanzar el consenso. • La combinación de técnicas de consenso distribuido y estructura y movimiento en tareas de control y percepción. Se ha diseñado un algoritmo para construir un mapa topológico de manera cooperativa usando planos como características del mapa y restricciones de homografía como elementos para relacionar las observaciones de los robots. También se ha propuesto una ley de control distribuida utilizando la geometría epipolar con el objetivo de hacer que el equipo de robots alcance una orientación común sin la necesidad de observarse directamente unos a otros

Department of Computer Science Activity 1998-2004

This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period

Unmanned Ground Vehicle navigation and coverage hole patching in Wireless Sensor Networks

This dissertation presents a study of an Unmanned Ground Vehicle (UGV) navigation and coverage hole patching in coordinate-free and localization-free Wireless Sensor Networks (WSNs). Navigation and coverage maintenance are related problems since coverage hole patching requires effective navigation in the sensor network environment. A coordinate-free and localization-free WSN that is deployed in an ad-hoc fashion and does not assume the availability of GPS information is considered. The system considered is decentralized and can be self-organized in an event-driven manner where no central controller or global map is required. A single-UGV, single-destination navigation problem is addressed first. The UGV is equipped with a set of wireless listeners that determine the slope of a navigation potential field generated by the wireless sensor and actuator network. The navigation algorithm consists of sensor node level-number assignment that is determined based on a hop-distance from the network destination node and UGV navigation through the potential field created by triplets of actuators in the network. A multi-UGV, multi-destination navigation problem requires a path-planning and task allocation process. UGVs inform the network about their proposed destinations, and the network provides feedback if conflicts are found. Sensor nodes store, share, and communicate to UGVs in order to allocate the navigation tasks. A special case of a single-UGV, multi-destination navigation problem that is equivalent to the well-known Traveling Salesman Problem is discussed. The coverage hole patching process starts after a UGV reaches the hole boundary. For each hole boundary edge, a new node is added along its perpendicular bisector, and the entire hole is patched by adding nodes around the hole boundary edges. The communication complexity and present simulation examples and experimental results are analyzed. Then, a Java-based simulation testbed that is capable of simulating both the centralized and distributed sensor and actuator network algorithms is developed. The laboratory experiment demonstrates the navigation algorithm (single-UGV, single-destination) using Cricket wireless sensors and an actuator network and Pioneer 3-DX robot

Sensor-Based Topological Coverage And Mapping Algorithms For Resource-Constrained Robot Swarms

Coverage is widely known in the field of sensor networks as the task of deploying sensors to completely cover an environment with the union of the sensor footprints. Related to coverage is the task of exploration that includes guiding mobile robots, equipped with sensors, to map an unknown environment (mapping) or clear a known environment (searching and pursuit- evasion problem) with their sensors. This is an essential task for robot swarms in many robotic applications including environmental monitoring, sensor deployment, mine clearing, search-and-rescue, and intrusion detection. Utilizing a large team of robots not only improves the completion time of such tasks, but also improve the scalability of the applications while increasing the robustness to systems’ failure. Despite extensive research on coverage, mapping, and exploration problems, many challenges remain to be solved, especially in swarms where robots have limited computational and sensing capabilities. The majority of approaches used to solve the coverage problem rely on metric information, such as the pose of the robots and the position of obstacles. These geometric approaches are not suitable for large scale swarms due to high computational complexity and sensitivity to noise. This dissertation focuses on algorithms that, using tools from algebraic topology and bearing-based control, solve the coverage related problem with a swarm of resource-constrained robots. First, this dissertation presents an algorithm for deploying mobile robots to attain a hole-less sensor coverage of an unknown environment, where each robot is only capable of measuring the bearing angles to the other robots within its sensing region and the obstacles that it touches. Next, using the same sensing model, a topological map of an environment can be obtained using graph-based search techniques even when there is an insufficient number of robots to attain full coverage of the environment. We then introduce the landmark complex representation and present an exploration algorithm that not only is complete when the landmarks are sufficiently dense but also scales well with any swarm size. Finally, we derive a multi-pursuers and multi-evaders planning algorithm, which detects all possible evaders and clears complex environments

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Topological Data Analysis in Sub-cellular Motion Reconstruction and Filament Networks Classification

Topological Data Analysis is a powerful tool in the image data analysis. In this dissertation, we focus on studying cell physiology by the sub-cellular motions of organelles and generation process of filament networks, relying on topology of the cellular image data. We first develop a novel, automated algorithm, which tracks organelle movements and reconstructs their trajectories on stacks of microscopy image data. Our tracking method proceeds with three steps: (i) identification, (ii) localization, and (iii) linking, and does not assume a specific motion model. This method combines topological data analysis principles with Ensemble Kalman Filtering in the computation of associated nerve during the linking step. Moreover, we show a great success of our method with several applications. We then study filament networks as a classification problem, and propose a distancebased classifier. This algorithm combines topological data analysis with a supervised machine learning framework, and is built based on the foundation of persistence diagrams on the data.We adopt a new metric, the dcp distance, on the space of persistence diagrams, and show it is useful in catching the geometric difference of filament networks. Furthermore, our classifier succeeds in classifying filament networks with high accuracy rate

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts