Enclosing a moving target with an optimally rotated and scaled multiagent pattern

We propose a novel control method to enclose a moving target in a two-dimensional setting with a team of agents forming a prescribed geometric pattern. The approach optimises a measure of the overall agent motion costs, via the minimisation of a suitably defined cost function encapsulating the pattern rotation and scaling. We propose two control laws which use global information and make the agents exponentially converge to the prescribed formation with an optimal scale that remains constant, while the team's centroid tracks the target. One control law results in a multiagent pattern that keeps a constant orientation in the workspace; for the other, the pattern rotates with constant speed. These behaviours, whose optimality and steadiness are very relevant for the task addressed, occur independently from the target's velocity. Moreover, the methodology does not require distance measurements, common coordinate references, or communications. We also present formal guarantees of collision avoidance for the proposed approach. Illustrative simulation examples are provided

Clustering-Based Robot Navigation and Control

In robotics, it is essential to model and understand the topologies of configuration spaces in order to design provably correct motion planners. The common practice in motion planning for modelling configuration spaces requires either a global, explicit representation of a configuration space in terms of standard geometric and topological models, or an asymptotically dense collection of sample configurations connected by simple paths, capturing the connectivity of the underlying space. This dissertation introduces the use of clustering for closing the gap between these two complementary approaches. Traditionally an unsupervised learning method, clustering offers automated tools to discover hidden intrinsic structures in generally complex-shaped and high-dimensional configuration spaces of robotic systems. We demonstrate some potential applications of such clustering tools to the problem of feedback motion planning and control. The first part of the dissertation presents the use of hierarchical clustering for relaxed, deterministic coordination and control of multiple robots. We reinterpret this classical method for unsupervised learning as an abstract formalism for identifying and representing spatially cohesive and segregated robot groups at different resolutions, by relating the continuous space of configurations to the combinatorial space of trees. Based on this new abstraction and a careful topological characterization of the associated hierarchical structure, a provably correct, computationally efficient hierarchical navigation framework is proposed for collision-free coordinated motion design towards a designated multirobot configuration via a sequence of hierarchy-preserving local controllers. The second part of the dissertation introduces a new, robot-centric application of Voronoi diagrams to identify a collision-free neighborhood of a robot configuration that captures the local geometric structure of a configuration space around the robot’s instantaneous position. Based on robot-centric Voronoi diagrams, a provably correct, collision-free coverage and congestion control algorithm is proposed for distributed mobile sensing applications of heterogeneous disk-shaped robots; and a sensor-based reactive navigation algorithm is proposed for exact navigation of a disk-shaped robot in forest-like cluttered environments. These results strongly suggest that clustering is, indeed, an effective approach for automatically extracting intrinsic structures in configuration spaces and that it might play a key role in the design of computationally efficient, provably correct motion planners in complex, high-dimensional configuration spaces

Target Assignment in Robotic Networks: Distance Optimality Guarantees and Hierarchical Strategies

We study the problem of multi-robot target assignment to minimize the total distance traveled by the robots until they all reach an equal number of static targets. In the first half of the paper, we present a necessary and sufficient condition under which true distance optimality can be achieved for robots with limited communication and target-sensing ranges. Moreover, we provide an explicit, non-asymptotic formula for computing the number of robots needed to achieve distance optimality in terms of the robots' communication and target-sensing ranges with arbitrary guaranteed probabilities. The same bounds are also shown to be asymptotically tight. In the second half of the paper, we present suboptimal strategies for use when the number of robots cannot be chosen freely. Assuming first that all targets are known to all robots, we employ a hierarchical communication model in which robots communicate only with other robots in the same partitioned region. This hierarchical communication model leads to constant approximations of true distance-optimal solutions under mild assumptions. We then revisit the limited communication and sensing models. By combining simple rendezvous-based strategies with a hierarchical communication model, we obtain decentralized hierarchical strategies that achieve constant approximation ratios with respect to true distance optimality. Results of simulation show that the approximation ratio is as low as 1.4

Algoritmo bioinspirado a redes de robots para la asistencia en operaciones de busqueda y rescate

ilustraciones, diagramas, fotografíasThis thesis proposes a bio-inspired algorithm for robot networks assisting in the operations of search and rescue scenarios. We consider ants as social animals to study and abstract beha- viors that can be useful in the framework of search and rescue using robots. We consider three main topics to address when using robots to assist rescuers. First, the exploration and mapping of the disaster zones. For this, we consider the mecha- nisms and interactions of ants to explore their environment, look for food, avoid predators, and explore better places to establish a nest. Then, we deploy robots to explore the en- vironment and discourage robots from entering regions other robots have explored using pheromones as markers for the robots. We also abstract the randomness ants use to explore and implement a Q-learning algorithm that allows robots to explore unvisited regions. Second, the navigation and victim detection. Once the environment has been explored, we vi use Reynolds rules to allow the navigation of robots to create cohesion, attraction to target goals, and repulsion to obstacles and inter-agent collisions. Then, we use a neural network to determine whether what robots are detecting is a victim. Lastly, we use a consensus-like approach to classify victims or no victims based on distributed information. Lastly, ants have been famous for carrying loads that surpass their size and payload capacity by cooperating. We consider quadrotors to carry loads cooperatively that can be medical supplies or victims in search and rescue (Texto tomado de la fuente)Esta tesis propone un algoritmo bioinspirado para redes de robots que asisten en las operaciones de escenarios de busqueda y rescate. Consideramos a las hormigas como animales sociales para estudiar y abstraer comportamientos que pueden ser utiles en el marco de la busqueda y rescate mediante robots. Consideramos tres temas principales para abordar cuando se utilizan robots para ayudar a los rescatistas. Primero, la exploracion y mapeo de las zonas de desastre. Para esto, consideramos los mecanismos e interacciones de las hormigas para explorar su entorno, buscar comida, evitar depredadores y explorar mejores lugares para establecer un nido. Luego, desplegamos robots para explorar el entorno y disuadimos a los robots de ingresar a regiones que otros robots han explorado usando feromonas como marcadores para los robots. Tambien abstraemos la aleatoriedad que usan las hormigas para explorar e implementar un algoritmo Q-learning que permite a los robots explorar regiones no visitadas. En segundo lugar, la navegacion y deteccion de vıctimas. Una vez que se ha explorado el entorno, usamos las reglas de Reynolds para permitir que la navegacion de los robots cree cohesion, atraccion hacia los objetivos y repulsion hacia los obstaculos y las colisiones entre agentes. Luego, usamos una red neuronal para determinar si lo que detectan los robots es una vıctima. Por ultimo, utilizamos un enfoque de consenso para clasificar a las vıctimas o no vıctimas en funcion de la informacion distribuida. Por ultimo, las hormigas han sido famosas por llevar cargas que superan su tamano y capacidad de carga al cooperar. Consideramos quadrotors para transportar cargas de manera cooperativa que pueden ser suministros medicos o vıctimas en busqueda y rescate.MaestríaMagister en Ingenieria - Automatizacion IndustrialRobotic

Probablistic approaches for intelligent AUV localisation

This thesis studies the problem of intelligent localisation for an autonomous underwater vehicle (AUV). After an introduction about robot localisation and specific issues in the underwater domain, the thesis will focus on passive techniques for AUV localisation, highlighting experimental results and comparison among different techniques. Then, it will develop active techniques, which require intelligent decisions about the steps to undertake in order for the AUV to localise itself. The undertaken methodology consisted in three stages: theoretical analysis of the problem, tests with a simulation environment, integration in the robot architecture and field trials. The conclusions highlight applications and scenarios where the developed techniques have been successfully used or can be potentially used to enhance the results given by current techniques. The main contribution of this thesis is in the proposal of an active localisation module, which is able to determine the best set of action to be executed, in order to maximise the localisation results, in terms of time and efficiency

Obstacle Avoidance for a Game Theoretically Controlled Formation of Unmanned Vehicles

The thesis provides a game theoretical approach to the control of a formation of unmanned vehicles. The objectives of the formation are to follow a prescribed trajectory, avoiding obstacle(s) while maintaining the geometry of the formation. Formation control is implemented using game theory while obstacles are avoided using Null Space Based Behavioral Control algorithm. Different obstacle avoidance scenarios are analyzed and compared. Numerical simulation results are presented, to validate the proposed approach

対象物体と指配置のコンフィグレーション空間を用いた不確かさを扱える効率的なケージング計画

学位の種別:課程博士University of Tokyo(東京大学

Collaborative decision making in uncertain environments

Two major issues in the design of multi-robot systems are those of communication and co-ordination. Communication ithin real world environments cannot always be guaranteed. A multi-robot system must, therefore, be able to continue with its task in the absence of communication between team members. Co-ordination of multiple robots to perform a specific task involves team members being able to make decisions as a single entity and as a member of a team. The co-ordination needs to be robust enough to handle failures within the system and unknown phenomena within the environment. In this thesis, the problems of communication and co-ordination are discussed and a new type of multi-robot system is introduced in an effort to solve the inherent difficulties within communication and co-ordination of multi-robot systems. The co-ordination and communication strategy is based upon the concept of sharing potential field information within dynamic local groups. Each member of the multi-robot system creates their own potential field based upon individual sensor readings. Team members that are dynamically assigned to local groups share their individual potential fields, in order to create a combined potential field which reduces the effect of sensor noise. It is because of this, that team members are able to make better decisions. A number of experiments, both in simulation and in laboratory environments, are presented. These experiments compare the performance of the system against a nonsharing control and a hybrid system made up of a global path planner and a reactive motor controller. It is demonstrated that the new system significantly outperforms these other methods in a search type problem. From this, it is concluded that the novel system proposed in this thesis successfully tackled the search problem, and that it should also be possible for the system to be applied to a number of other common multi-robot problems

Probe arm motion techniques for miradas multi-object spectrograph

Desde tiempos remotos, los humanos se han sentido atraídos por los objetos brillantes que pueblan el cielo. A pesar de lo mucho que sabemos actualmente sobre ellos, quedan por desentrañar varios misterios que aún esconde la Vía Láctea. ¿Cómo se formó? ¿Cómo ha cambiado a través del tiempo? Estas son sólo dos de las múltiples preguntas para las que la astrofísica moderna no tiene respuesta. Los científicos han estado construyendo diferentes modelos que intentan simular la evolución de nuestra galaxia. Sin embargo, muchas más observaciones son necesarias para poder dar valores razonables a las diversas variables presentes en esos modelos. Fruto de esta búsqueda, en las últimas décadas se han destinado muchos esfuerzos al desarrollo de nuevas instalaciones de espectroscopía multi-objeto. El Mid-resolution InfRAreD Astronomical Spectrograph (MIRADAS) es un espectrógrafo Echelle multi-objeto en el infrarrojo cercano para el Gran Telescopio Canarias (GTC) diseñado por un consorcio internacional. Gracias a su potente resolución y a su capacidad de multiplexación, este instrumento será clave para abordar algunos de los principales desafíos científicos de las siguientes décadas. MIRADAS, con la ayuda de sus 12 unidades autónomas de campo integral, observará simultáneamente hasta 12 objetivos celestes ubicados en distintos puntos del cielo. Cada una de estas unidades opto-mecánicos tiene la forma de un brazo robótico. Las estructuras de estos dispositivos han estado especialmente concebidas para: (i) asegurar la simplicidad del camino óptico (ii) ofrecer un gran grado de estabilidad cuando el brazo trabaje invertido. Pero, desafortunadamente, el costo de este diseño se traduce en un comportamiento del brazo complejo y nada intuitivo. En esta tesis, incluimos un estudio exhaustivo del brazo robótico de MIRADAS. En concreto, se presenta un modelo matemático, así como soluciones para los problemas de cinemática directa e inversa. Además, también se analizan las particularidades que limitan su movimiento. Primero, se estudia su espacio de articular y las regiones prohibidas del mismo debido a la naturaleza del brazo. En segundo lugar, se aborda como la incapacidad de interpolación de los controladores que gobiernan los actuadores afecta a la generación de trayectorias. Finalmente, se discuten varias estrategias de patrullaje, prestando especial atención a sus ventajas y puntos débiles. Por otro lado, los brazos de MIRADAS están distribuidos alrededor de una plataforma circular en la que no hay mucho espacio. Así pues, con el fin adquirir los objetivos requeridos por el usuario, los brazos del sistema deben moverse extremo cuidado. En MIRADAS, el cómputo de trayectorias se ha dividido en tres procesos diferentes, tratados todos en detalle en este trabajo. El primero de ellos, la segmentación de campo, organiza los distintos objetivos presentes en un campo disperso de estrellas de tal manera que estos puedan ser observados adecuadamente. Específicamente, esta fase calcula varios grupos, los integrantes de los cuales se emplean posteriormente en la etapa de asignación de brazos. Adicionalmente, también se determina el centro geométrico de cada uno de estos grupos, información requerida para apuntar correctamente el telescopio.Con respecto a la asignación de brazos, etapa responsable de determinar la asociación más adecuada . La primera se basa en programación lineal y, como demuestran las pruebas, es la que obtiene mejores resultados en términos de las diferentes métricas utilizadas. Sin embargo, este método deja de ser práctico cuando se tiene enfrente campos grandes. En estos escenarios, la segunda solución, estructurada entorno a una metaheurística, obtiene buenos resultados en un tiempo de ejecución asumible. Finalmente, los planes de asignación resultantes se pasan a un planificador de trayectorias. El planificador de trayectorias es el tercer y último paso del proceso. Este analiza individualmente cada plan de asignación, devolviendo movimientos coordinados para todos los brazos involucrados. Estos movimientos, una vez traducidos a instrucciones de bajo nivel y ejecutados por el software que controla los correspondientes actuadores, colocarán el espejo de cada brazo en en la ubicación del cielo correspondiente. Las pruebas experimentales muestran que el planificador es capaz de calcular movimientos exitosos. Esto es así tanto en un escenario típico en el que se producen varias instancies de los dos tipos de conflictos que puede surgir en MIRADAS como en una serie de escenarios con objetivos científicos reales