Intermittent Connectivity for Exploration in Communication-Constrained Multi-Agent Systems

Motivated by exploration of communication-constrained underground environments using robot teams, we study the problem of planning for intermittent connectivity in multi-agent systems. We propose a novel concept of information-consistency to handle situations where the plan is not initially known by all agents, and suggest an integer linear program for synthesizing information-consistent plans that also achieve auxiliary goals. Furthermore, inspired by network flow problems we propose a novel way to pose connectivity constraints that scales much better than previous methods. In the second part of the paper we apply these results in an exploration setting, and propose a clustering method that separates a large exploration problem into smaller problems that can be solved independently. We demonstrate how the resulting exploration algorithm is able to coordinate a team of ten agents to explore a large environment

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

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 fuzzified systematic adjustment of the robotic Darwinian PSO

The Darwinian Particle Swarm Optimization (DPSO) is an evolutionary algorithm that extends the Particle Swarm Optimization using natural selection to enhance the ability to escape from sub-optimal solutions. An extension of the DPSO to multi-robot applications has been recently proposed and denoted as Robotic Darwinian PSO (RDPSO), benefiting from the dynamical partitioning of the whole population of robots, hence decreasing the amount of required information exchange among robots. This paper further extends the previously proposed algorithm adapting the behavior of robots based on a set of context-based evaluation metrics. Those metrics are then used as inputs of a fuzzy system so as to systematically adjust the RDPSO parameters (i.e., outputs of the fuzzy system), thus improving its convergence rate, susceptibility to obstacles and communication constraints. The adapted RDPSO is evaluated in groups of physical robots, being further explored using larger populations of simulated mobile robots within a larger scenario

Intermittent Connectivity for Exploration in Communication-Constrained Multi-Agent Systems

Motivated by exploration of communication-constrained underground environments using robot teams, we study the problem of planning for intermittent connectivity in multi-agent systems. We propose a novel concept of information-consistency to handle situations where the plan is not initially known by all agents, and suggest an integer linear program for synthesizing information-consistent plans that also achieve auxiliary goals. Furthermore, inspired by network flow problems we propose a novel way to pose connectivity constraints that scales much better than previous methods. In the second part of the paper we apply these results in an exploration setting, and propose a clustering method that separates a large exploration problem into smaller problems that can be solved independently. We demonstrate how the resulting exploration algorithm is able to coordinate a team of ten agents to explore a large environment

Self-management Framework for Mobile Autonomous Systems

The advent of mobile and ubiquitous systems has enabled the development of autonomous systems such as wireless-sensors for environmental data collection and teams of collaborating Unmanned Autonomous Vehicles (UAVs) used in missions unsuitable for humans. However, with these range of new application domains comes a new challenge – enabling self-management in mobile autonomous systems. The primary challenge in using autonomous systems for real-life missions is shifting the burden of management from humans to these systems themselves without loss of the ability to adapt to failures, changes in context, and changing user requirements. Autonomous systems have to be able to manage themselves individually as well as to form self-managing teams that are able to recover or adapt to failures, protect themselves from attacks and optimise performance. This thesis proposes a novel distributed policy-based framework that enables autonomous systems to perform self management individually and as a team. The framework allows missions to be specified in terms of roles in an adaptable and reusable way, enables dynamic and secure team formation with a utility-based approach for optimal role assignment, caters for communication link maintenance among team members and recovery from failure. Adaptive management is achieved by employing an architecture that uses policy-based techniques to allow dynamic modification of the management strategy relating to resources, role behaviour, team and communications management, without reloading the basic software within the system. Evaluation of the framework shows that it is scalable with respect to the number of roles, and consequently the number of autonomous systems participating in the mission. It is also shown to be optimal with respect to role assignments, and robust to intermittent communication link disconnections and permanent team-member failures. The prototype implementation was tested on mobile robots as a proof-ofconcept demonstration