Design Methodology for Unmanned Aerial Vehicle (UAV) Team Coordination

Unmanned Aerial Vehicle (UAV) systems, despite having no onboard human pilots, currently require extensive human involvement to accomplish successful mission operations. Further, successful operations also require extensive colalboration between mission stakeholders, including operators, mission commanders, and information consumers (e.g. ground troops relying on intelligence reports in their area). Existing UAV system interfaces provide little to no support for collaboration between remote operators or for operators to collaborate with information consumers. As reliance on UAVs continues to increase in military and civilian operations, this lack of support for collaboration will likely become a substantial limitation of existing UAV systems. In order to introduce effective collaboration support to UAV system interfaces, it is essential to understand, and be able to derive system design requirements that address, the necessary group interactions that occur in UAV task enviroments. However, few collaborative requirements analysis methods exist, and to our knowledge, no method exists that captures design requirements for collaborative decision making in complex, time-critical environments. This report describes the development of a new design requirements analysis method for deriving information and functional requirements that address the collaboration needs of UAV (and other complex task) operators, and the needs of stakeholders interacting with these operators. More specifically, theis method extends a recently developed requirements analysis method, called the Hybrid Cognitive Task Analysis (CTA) method, which enables the generation of information and functional requirements for futuristic UAV system interfaces. The original Hybrid CTA method focused on deriving single user system interface requirements. This work extends this method by introducing analytic steps to identify task and decision-making dependencies between different UAV operations collaborators. This collaborative extension to the Hybrid CTA utilizes the notion of boundary objects, an analytic construct commonly used in the study of group work. Boundary objects are physical or information artifacts that cross the task boundaries between members of distinct groups. Identifying boundary objects in complex task operations help the analyst to identify task and decision-making dependencies between local and remote collaborators. Understanding these dependencies helps to identify information sharing requirements that the UAV system should support. This report describes the analytic steps of the collaborative extension, and provides background information on the original Hybrid CTA method and the boundary object construct. The report also describes a project in which the new design requirements method was used to revise a proposed set of UAV operator displays.Prepared For Boeing Phantom Work

Adoption of vehicular ad hoc networking protocols by networked robots

This paper focuses on the utilization of wireless networking in the robotics domain. Many researchers have already equipped their robots with wireless communication capabilities, stimulated by the observation that multi-robot systems tend to have several advantages over their single-robot counterparts. Typically, this integration of wireless communication is tackled in a quite pragmatic manner, only a few authors presented novel Robotic Ad Hoc Network (RANET) protocols that were designed specifically with robotic use cases in mind. This is in sharp contrast with the domain of vehicular ad hoc networks (VANET). This observation is the starting point of this paper. If the results of previous efforts focusing on VANET protocols could be reused in the RANET domain, this could lead to rapid progress in the field of networked robots. To investigate this possibility, this paper provides a thorough overview of the related work in the domain of robotic and vehicular ad hoc networks. Based on this information, an exhaustive list of requirements is defined for both types. It is concluded that the most significant difference lies in the fact that VANET protocols are oriented towards low throughput messaging, while RANET protocols have to support high throughput media streaming as well. Although not always with equal importance, all other defined requirements are valid for both protocols. This leads to the conclusion that cross-fertilization between them is an appealing approach for future RANET research. To support such developments, this paper concludes with the definition of an appropriate working plan

MusA: Using Indoor Positioning and Navigation to Enhance Cultural Experiences in a museum

In recent years there has been a growing interest into the use of multimedia mobile guides in museum environments. Mobile devices have the capabilities to detect the user context and to provide pieces of information suitable to help visitors discovering and following the logical and emotional connections that develop during the visit. In this scenario, location based services (LBS) currently represent an asset, and the choice of the technology to determine users' position, combined with the definition of methods that can effectively convey information, become key issues in the design process. In this work, we present MusA (Museum Assistant), a general framework for the development of multimedia interactive guides for mobile devices. Its main feature is a vision-based indoor positioning system that allows the provision of several LBS, from way-finding to the contextualized communication of cultural contents, aimed at providing a meaningful exploration of exhibits according to visitors' personal interest and curiosity. Starting from the thorough description of the system architecture, the article presents the implementation of two mobile guides, developed to respectively address adults and children, and discusses the evaluation of the user experience and the visitors' appreciation of these application

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

Mission-based mobility models for UAV networks

Las redes UAV han atraído la atención de los investigadores durante la última década. Las numerosas posibilidades que ofrecen los sistemas single-UAV aumentan considerablemente al usar múltiples UAV. Sin embargo, el gran potencial del sistema multi-UAV viene con un precio: la complejidad de controlar todos los aspectos necesarios para garantizar que los UAVs cumplen la misión que se les ha asignado. Ha habido numerosas investigaciones dedicadas a los sistemas multi-UAV en el campo de la robótica en las cuales se han utilizado grupos de UAVs para diferentes aplicaciones. Sin embargo, los aspectos relacionados con la red que forman estos sistemas han comenzado a reclamar un lugar entre la comunidad de investigación y han hecho que las redes de UAVs se consideren como un nuevo paradigma entre las redes multi-salto. La investigación de redes de UAVs, de manera similar a otras redes multi-salto, se divide principalmente en dos categorías: i) modelos de movilidad que capturan la movilidad de la red, y ii) algoritmos de enrutamiento. Ambas categorías han heredado muchos algoritmos que pertenecían a las redes MANET, que fueron el primer paradigma de redes multi-salto que atrajo la atención de los investigadores. Aunque hay esfuerzos de investigación en curso que proponen soluciones para ambas categorías, el número de modelos de movilidad y algoritmos de enrutamiento específicos para redes UAV es limitado. Además, en el caso de los modelos de movilidad, las soluciones existentes propuestas son simplistas y apenas representan la movilidad real de un equipo de UAVs, los cuales se utilizan principalmente en operaciones orientadas a misiones, en la que cada UAV tiene asignados movimientos específicos. Esta tesis propone dos modelos de movilidad basados en misiones para una red de UAVs que realiza dos operaciones diferentes. El escenario elegido en el que se desarrollan las misiones corresponde con una región en la que ha ocurrido, por ejemplo, un desastre natural. La elección de este tipo de escenario se debe a que en zonas de desastre, la infraestructura de comunicaciones comúnmente está dañada o totalmente destruida. En este tipo de situaciones, una red de UAVs ofrece la posibilidad de desplegar rápidamente una red de comunicaciones. El primer modelo de movilidad, llamado dPSO-U, ha sido diseñado para capturar la movilidad de una red UAV en una misión con dos objetivos principales: i) explorar el área del escenario para descubrir las ubicaciones de los nodos terrestres, y ii) hacer que los UAVs converjan de manera autónoma a los grupos en los que se organizan los nodos terrestres (también conocidos como clusters). El modelo de movilidad dPSO-U se basa en el conocido algoritmo particle swarm optimization (PSO), considerando los UAV como las partículas del algoritmo, y también utilizando el concepto de valores dinámicos para la inercia, el local best y el neighbour best de manera que el modelo de movilidad tenga ambas capacidades: la de exploración y la de convergencia. El segundo modelo, denominado modelo de movilidad Jaccard-based, captura la movilidad de una red UAV que tiene asignada la misión de proporcionar servicios de comunicación inalámbrica en un escenario de mediano tamaño. En este modelo de movilidad se ha utilizado una combinación del virtual forces algorithm (VFA), de la distancia Jaccard entre cada par de UAVs y metaheurísticas como hill climbing y simulated annealing, para cumplir los dos objetivos de la misión: i) maximizar el número de nodos terrestres (víctimas) que se encuentran bajo el área de cobertura inalámbrica de la red UAV, y ii) mantener la red UAV como una red conectada, es decir, evitando las desconexiones entre UAV. Se han realizado simulaciones exhaustivas con herramientas software específicamente desarrolladas para los modelos de movilidad propuestos. También se ha definido un conjunto de métricas para cada modelo de movilidad. Estas métricas se han utilizado para validar la capacidad de los modelos de movilidad propuestos de emular los movimientos de una red UAV en cada misión.UAV networks have attracted the attention of the research community in the last decade. The numerous capabilities of single-UAV systems increase considerably by using multiple UAVs. The great potential of a multi-UAV system comes with a price though: the complexity of controlling all the aspects required to guarantee that the UAV team accomplish the mission that it has been assigned. There have been numerous research works devoted to multi-UAV systems in the field of robotics using UAV teams for different applications. However, the networking aspects of multi-UAV systems started to claim a place among the research community and have made UAV networks to be considered as a new paradigm among the multihop ad hoc networks. UAV networks research, in a similar manner to other multihop ad hoc networks, is mainly divided into two categories: i) mobility models that capture the network mobility, and ii) routing algorithms. Both categories have inherited previous algorithms mechanisms that originally belong to MANETs, being these the first multihop networking paradigm attracting the attention of researchers. Although there are ongoing research efforts proposing solutions for the aforementioned categories, the number of UAV networks-specific mobility models and routing algorithms is limited. In addition, in the case of the mobility models, the existing solutions proposed are simplistic and barely represent the real mobility of a UAV team, which are mainly used in missions-oriented operations. This thesis proposes two mission-based mobility models for a UAV network carrying out two different operations over a disaster-like scenario. The reason for selecting a disaster scenario is because, usually, the common communication infrastructure is malfunctioning or completely destroyed. In these cases, a UAV network allows building a support communication network which is rapidly deployed. The first mobility model, called dPSO-U, has been designed for capturing the mobility of a UAV network in a mission with two main objectives: i) exploring the scenario area for discovering the location of ground nodes, and ii) making the UAVs to autonomously converge to the groups in which the nodes are organized (also referred to as clusters). The dPSO-U mobility model is based on the well-known particle swarm optimization algorithm (PSO), considering the UAVs as the particles of the algorithm, and also using the concept of dynamic inertia, local best and neighbour best weights so the mobility model can have both abilities: exploration and convergence. The second one, called Jaccard-based mobility model, captures the mobility of a UAV network that has been assigned with the mission of providing wireless communication services in a medium-scale scenario. A combination of the virtual forces algorithm (VFA), the Jaccard distance between each pair of UAVs and metaheuristics such as hill climbing or simulated annealing have been used in this mobility model in order to meet the two mission objectives: i) to maximize the number of ground nodes (i.e. victims) under the UAV network wireless coverage area, and ii) to maintain the UAV network as a connected network, i.e. avoiding UAV disconnections. Extensive simulations have been performed with software tools that have been specifically developed for the proposed mobility models. Also, a set of metrics have been defined and measured for each mobility model. These metrics have been used for validating the ability of the proposed mobility models to emulate the movements of a UAV network in each mission