Optimal role and position assignment in multi-robot freely reachable formations

Many multi-robot problems require the achievement of formations as part of the overall mission. This work considers a scenario in which unlabeled homogeneous robots must adopt a given formation pattern buildable anywhere in the environment. This involves finding the relative pose of the formation in regard to the initial robot positions, understood as a translation and a rotation; and the optimal assignment of the role of each robot within the formation. This paper provides an optimal solution for the combined parameters of translation, rotation and assignment that minimizes total displacement. To achieve this objective we first formally prove that the three decision variables are separable. Since computing the optimal assignment without accounting for the rotation is a computationally expensive problem, we propose an algorithm that efficiently computes the optimal roles together with the rotation. The algorithm is provably correct and finds the optimal solution in finite time. A distributed implementation is also discussed. Simulation results characterize the complexity of our solution and demonstrate its effectiveness

Modelo de estratégia e coordenação genérico para sistemas multi-agente

Estágio realizado na Universidade de Aveiro e orientado pelo Prof. Doutor Jose Nuno Panelas Nunes LauTese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Distributed Dynamic Sensor Assignment of Multiple Mobile Targets

Distributed scalable algorithms are sought in many multi-robot contexts. In this work we address the dynamic optimal linear assignment problem, exemplified as a target tracking mission in which mobile robots visually track mobile targets in a one-to-one capacity. We adapt our previous work on formation achievement by means of a distributed simplex variant, which results in a conceptually simple consensus solution, asynchronous in nature and requiring only local broadcast communications. This approach seamlessly tackles dynamic changes in both costs and network topology. Improvements designed to accelerate the global convergence in the face of dynamically evolving task rewards are described and evaluated with simulations that highlight the efficiency and scalability of the proposal. Experiments with a team of three Turtlebot robots are finally shown to validate the applicability of the algorithm

Multi-robot deployment planning in communication-constrained environments

A lo largo de los últimos años se ha podido observar el aumento del uso de equipos de robots en tareas en las cuales es imposible o poco eficiente la intervención de los humanos, e incluso que implica un cierto grado de riesgo para una persona. Por ejemplo, monitorización de entornos de difícil acceso, como podrían ser túneles, minas, etc. Éste es el tema en el que se ha enfocado el trabajo realizado durante esta tesis: la planificación del despliegue de un equipo de agentes para la monitorización de entornos.La misión de los agentes es alcanzar unas localizaciones de interés y transmitirle la información observada a una estación base estática. Ante la ausencia de una infraestructura de comunicaciones, una transmisión directa a la base es imposible. Por tanto, los agentes se deben coordinar de manera autónoma, de modo que algunos de ellos alcancen los objetivos y otros realicen la función de repetidor para retransmitir la información.Nos hemos centrado en dos líneas de investigación principales, relacionadas con dos maneras del envío de la información a la estación base. En el primer enfoque, los agentes deben mantener un enlace de comunicación con la base en el momento de alcanzar los objetivos. Con el fin de, por ejemplo, poder interactuar desde la base con un robot que ha alcanzado el objetivo. Para ello hemos desarrollado un método que obtiene las posiciones óptimas para los agentes utilizados a modo de repetidor. A continuación, hemos implementado un método de planificación de caminos de modo que los agentes pudiesen navegar el máximo tiempo posible dentro de zonas con señal. Empleando conjuntamente ambos métodos, los agentes extienden el área de cobertura de la estación base, estableciendo un enlace de comunicación desde la misma hasta los objetivos marcados.Utilizando este método, el equipo es capaz de lidiar con variaciones del entorno si la comunicación entre los agentes no se pierde. Sin embargo, los eventos tan comunes e irrelevantes para los seres humanos, como el simple cierre de una puerta, pueden llegar a ser críticos para el equipo de robots. Ya que esto podría interrumpir la comunicación entre el equipo. Por ello, hemos propuesto un método distribuido para que el equipo sea capaz de reconectarse, formando una cadena hacia un objetivo, en escenarios donde haya variaciones con respecto al mapa inicial que poseían los robots.La segunda parte de la presente tesis se ha centrado en misiones de recopilación de datos de un entorno. Aquí la comunicación con la estación base, en el instante de alcanzar un objetivo, no es necesaria y a menudo imposible. Por tanto, en este tipo de escenarios, es más eficiente que algunos agentes, llamados trabajadores, recopilen datos del entorno, y otros, denominados colectores, reúnan la información de los que trabajan para periódicamente retransmitirla a la base. De este modo tan solo los colectores realizan largos viajes a la estación base, mientras que los trabajadores emplean la mayor parte de su tiempo exclusivamente a la recopilación de datos.Primero, hemos desarrollado dos métodos para la planificación de caminos para la sincronización entre los trabajadores y colectores. El primero, muestrea el espacio de manera aleatoria, para obtener una solución lo más rápido posible. El segundo, usando FMM, es más lento, pero obtiene soluciones óptimas.Finalmente, hemos propuesto una técnica global para la misión de recopilación de datos. Este método consiste en: encontrar el mejor balance entre la cantidad de trabajadores y colectores, la mejor división del escenario en áreas de trabajo para los trabajadores, la asociación de los trabajadores para transmitir los datos recopilados a los colectores o directamente a la estación base, así como los caminos de los colectores. El método propuesto trata de encontrar la mejor solución con el fin de entregar la mayor cantidad de datos y que el tiempo de "refresco" de los mismos sea el menor posible.<br /

A control architecture and human interface for agile, reconfigurable micro aerial vehicle formations

This thesis considers the problem of controlling a group of micro aerial vehicles for agile maneuvering cooperatively, or distributively. We first introduce the background and motivation for micro aerial vehicles, especially for the popular multi-rotor aerial vehicle platform. Then, we discuss the dynamics of quadrotor helicopters. A quadrotor is a specific kind of multi-rotor aerial vehicle with a special property called differential flatness, which simplifies the algorithm of trajectory planning, such that, instead of planning a trajectory in a 12-dimensional state space and 4-dimensional input space, we only need to plan the trajectory in 4-dimensional, so called, flat output space, while the 12-dimensional state and 4-dimensional input can be recovered from a mapping called endogenous transformation. We propose a series of approaches to achieve agile maneuvering of a dynamic quadrotor formation, from controlling a single quadrotor in an artificial vector field, to controlling a group of quadrotors in a Virtual Rigid Body (VRB) framework, to balancing the effect between the human control and autonomy for collision avoidance, and to fast on-line distributed collision avoidance with Buffered Voronoi Cells (BVC). In the vector field method, we generate velocity, acceleration, jerk and snap fields, depending on the tasks, or the positions of obstacles, such that a single quadrotor can easily find its required state and input from the endogenous transformation in order to track the artificial vector field. Next, with a Virtual Rigid Body framework, we let a group of quadrotors follow a single control command while also keeping a required formation, or even reconfigure from one formation to another. The Virtual Rigid Body framework decouples the trajectory planning problem into two sub-problems. Then we consider the problem of collision avoidance of the quadrotor formation when it is meanwhile tele-operated by a single human operator. The autonomy with collision avoidance algorithm, based on the vector field methods for a single quadrotor, is an assistive portion of the quadrotor formation controller, such that the human operator can focus on his/her high-level tasks, leaving the low-level collision avoidance task be handled automatically. We also consider the full autonomy problem of quadrotor formations when reconfiguring from one formation to another by developing a fast, on-line distributed collision avoidance algorithm using Buffered Voronoi Cells (BVCs). Our BVC based collision avoidance algorithm only requires sensed relative position, rather than relative position and velocity, while the computational complexity is comparable to other methods like velocity obstacles. At last, we introduce our experimental quadrotor platform which is built from PixHawk flight controller and Odroid-XU4 single-board computer. The hardware and software architecture of this multiple-quadrotor platform is described in detail so that our platform can easily be adopted and extended with different purposes. Our conclusion remark and discussion of future work are also given in this thesi

A Tutorial on Distributed Optimization for Cooperative Robotics: from Setups and Algorithms to Toolboxes and Research Directions

Several interesting problems in multi-robot systems can be cast in the framework of distributed optimization. Examples include multi-robot task allocation, vehicle routing, target protection and surveillance. While the theoretical analysis of distributed optimization algorithms has received significant attention, its application to cooperative robotics has not been investigated in detail. In this paper, we show how notable scenarios in cooperative robotics can be addressed by suitable distributed optimization setups. Specifically, after a brief introduction on the widely investigated consensus optimization (most suited for data analytics) and on the partition-based setup (matching the graph structure in the optimization), we focus on two distributed settings modeling several scenarios in cooperative robotics, i.e., the so-called constraint-coupled and aggregative optimization frameworks. For each one, we consider use-case applications, and we discuss tailored distributed algorithms with their convergence properties. Then, we revise state-of-the-art toolboxes allowing for the implementation of distributed schemes on real networks of robots without central coordinators. For each use case, we discuss their implementation in these toolboxes and provide simulations and real experiments on networks of heterogeneous robots

Coordination of Multirobot Teams and Groups in Constrained Environments: Models, Abstractions, and Control Policies

Robots can augment and even replace humans in dangerous environments, such as search and rescue and reconnaissance missions, yet robots used in these situations are largely tele-operated. In most cases, the robots\u27 performance depends on the operator\u27s ability to control and coordinate the robots, resulting in increased response time and poor situational awareness, and hindering multirobot cooperation. Many factors impede extended autonomy in these situations, including the unique nature of individual tasks, the number of robots needed, the complexity of coordinating heterogeneous robot teams, and the need to operate safely. These factors can be partly addressed by having many inexpensive robots and by control policies that provide guarantees on convergence and safety. In this thesis, we address the problem of synthesizing control policies for navigating teams of robots in constrained environments while providing guarantees on convergence and safety. The approach is as follows. We first model the configuration space of the group (a space in which the robots cannot violate the constraints) as a set of polytopes. For a group with a common goal configuration, we reduce complexity by constructing a configuration space for an abstracted group state. We then construct a discrete representation of the configuration space, on which we search for a path to the goal. Based on this path, we synthesize feedback controllers, decentralized affine controllers for kinematic systems and nonlinear feedback controllers for dynamical systems, on the polytopes, sequentially composing controllers to drive the system to the goal. We demonstrate the use of this method in urban environments and on groups of dynamical systems such as quadrotors. We reduce the complexity of multirobot coordination by using an informed graph search to simultaneously build the configuration space and find a path in its discrete representation to the goal. Furthermore, by using an abstraction on groups of robots we dissociate complexity from the number of robots in the group. Although the controllers are designed for navigation in known environments, they are indeed more versatile, as we demonstrate in a concluding simulation of six robots in a partially unknown environment with evolving communication links, object manipulation, and stigmergic interactions

Mobile Robots

The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations