15 research outputs found

    Mission programming for flying ensembles: combining planning with self-organization

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    The application of autonomous mobile robots can improve many situations of our daily lives. Robots can enhance working conditions, provide innovative techniques for different research disciplines, and support rescue forces in an emergency. In particular, flying robots have already shown their potential in many use-cases when cooperating in ensembles. Exploiting this potential requires sophisticated measures for the goal-oriented, application-specific programming of flying ensembles and the coordinated execution of so defined programs. Because different goals require different robots providing different capabilities, several software approaches emerged recently that focus on specifically designed robots. These approaches often incorporate autonomous planning, scheduling, optimization, and reasoning attributable to classic artificial intelligence. This allows for the goal-oriented instruction of ensembles, but also leads to inefficiencies if ensembles grow large or face uncertainty in the environment. By leaving the detailed planning of executions to individuals and foregoing optimality and goal-orientation, the selforganization paradigm can compensate for these drawbacks by scalability and robustness. In this thesis, we combine the advantageous properties of autonomous planning with that of self-organization in an approach to Mission Programming for Flying Ensembles. Furthermore, we overcome the current way of thinking about how mobile robots should be designed. Rather than assuming fixed-design robots, we assume that robots are modifiable in terms of their hardware at run-time. While using such robots enables their application in many different use cases, it also requires new software approaches for dealing with this flexible design. The contributions of this thesis thus are threefold. First, we provide a layered reference architecture for physically reconfigurable robot ensembles. Second, we provide a solution for programming missions for ensembles consisting of such robots in a goal-oriented fashion that provides measures for instructing individual robots or entire ensembles as desired in the specific use case. Third, we provide multiple self-organization mechanisms to deal with the system’s flexible design while executing such missions. Combining different self-organization mechanisms ensures that ensembles satisfy the static requirements of missions. We provide additional self-organization mechanisms for coordinating the execution in ensembles ensuring they meet the dynamic requirements of a mission. Furthermore, we provide a solution for integrating goal-oriented swarm behavior into missions using a general pattern we have identified for trajectory-modification-based swarm behavior. Using that pattern, we can modify, quantify, and further process the emergent effect of varying swarm behavior in a mission by changing only the parameters of its implementation. We evaluate results theoretically and practically in different case studies by deploying our techniques to simulated and real hardware.Der Einsatz von autonomen mobilen Robotern kann viele AblĂ€ufe unseres tĂ€glichen Lebens erleichtern. Ihr Einsatz kann Arbeitsbedingungen verbessern, als innovative Technik fĂŒr verschiedene Forschungsdisziplinen dienen oder RettungskrĂ€fte im Einsatz unterstĂŒtzen. Insbesondere Flugroboter haben ihr Potenzial bereits in vielerlei AnwendungsfĂ€llen gezeigt, gerade wenn mehrere in Ensembles eingesetzt werden. Das Potenzial fliegender Ensembles zielgerichtet und anwendungsspezifisch auszuschöpfen erfordert ausgefeilte Programmiermethoden und Koordinierungsverfahren. Zu diesem Zweck sind zuletzt viele unterschiedliche und auf speziell entwickelte Roboter zugeschnittene SoftwareansĂ€tze entstanden. Diese verwenden oft klassische Planungs-, Scheduling-, Optimierungs- und Reasoningverfahren. WĂ€hrend dies vor allem den zielgerichteten Einsatz von Ensembles ermöglicht, ist es jedoch auch oft ineffizient, wenn die Ensembles grĂ¶ĂŸer oder deren Einsatzumgebungen unsicher werden. Die genannten Nachteile können durch das Paradigma der Selbstorganisation kompensiert werden: Falls Anwendungen nicht zwangslĂ€ufig auf OptimalitĂ€t und strikte Zielorientierung ausgelegt sind, kann so Skalierbarkeit und Robustheit im System erreicht werden. In dieser Arbeit werden die vorteilhaften Eigenschaften klassischer Planungstechniken mit denen der Selbstorganisation in einem Ansatz zur Missionsprogrammierung fĂŒr fliegende Ensembles kombiniert. In der dafĂŒr entwickelten Lösung wird von der aktuell etablierten Ansicht einer unverĂ€nderlichen Roboterkonstruktion abgewichen. Stattdessen wird die Hardwarezusammenstellung der Roboter als zur Laufzeit modifizierbar angesehen. Der Einsatz solcher Roboter erfordert neue SoftwareansĂ€tze um mit genannter FlexibilitĂ€t umgehen zu können. Die hier vorgestellten BeitrĂ€ge zu diesem Thema lassen sich in drei Punkten zusammenfassen: Erstens wird eine Schichtenarchitektur als Referenz fĂŒr physikalisch konfigurierbare Roboterensembles vorgestellt. Zweitens wird eine Lösung zur zielorientierten Missions-Programmierung fĂŒr derartige Ensembles prĂ€sentiert, mit der sowohl einzelne Roboter als auch ganze Ensembles instruiert werden können. Drittens werden mehrere Selbstorganisationsmechanismen vorgestellt, die die autonome AusfĂŒhrung so erstellter Missionen ermöglichen. Durch die Kombination verschiedener Selbstorganisationsmechanismen wird sichergestellt, dass Ensembles die missionsspezifischen Anforderungen erfĂŒllen. ZusĂ€tzliche Selbstorganisationsmechanismen ermöglichen die koordinierte AusfĂŒhrung der Missionen durch die Ensembles. DarĂŒber hinaus bietet diese Lösung die Möglichkeit der Integration zielorientierten Schwarmverhaltens. Durch ein allgemeines algorithmisches Verfahren fĂŒr auf Trajektorien-Modifikation basierendes Schwarmverhalten können allein durch die Änderung des Parametersatzes unterschiedliche emergente Effekte in einer Mission erzielt, quantifiziert und weiterverarbeitet werden. Zur theoretischen und praktischen Evaluierung der Ergebnisse dieser Arbeit wurden die vorgestellten Techniken in verschiedenen Fallstudien auf simulierter sowie realer Hardware zum Einsatz gebracht

    Towards adaptive multi-robot systems: self-organization and self-adaptation

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    Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugÀnglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible

    Maple-Swarm: programming collective behavior for ensembles by extending HTN-planning

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    Programming goal-oriented behavior in collective adaptive systems is complex, requires high effort, and is failure-prone. If the system's user wants to deploy it in a real-world environment, hurdles get even higher: Programs urgently require to be situation-aware. With our framework Maple, we previously presented an approach for easing the act of programming such systems on the level of particular robot capabilities. In this paper, we extend our approach for ensemble programming with the possibility to address virtual swarm capabilities encapsulating collective behavior to whole groups of agents. By using the respective concepts in an extended version of hierarchical task networks and by adapting our self-organization mechanisms for executing plans resulting thereof, we can achieve that all agents, any agent, any other set of agents, or a swarm of agents execute (swarm) capabilities. Moreover, we extend the possibilities of expressing situation awareness during planning by introducing planning variables that can get modified at design-time or run-time as needed. We illustrate the possibilities with examples each. Further, we provide a graphical front-end offering the possibility to generate mission-specific problem domain descriptions for ensembles including a lightweight simulation for validating plans

    Organization based multiagent architecture for distributed environments

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    [EN]Distributed environments represent a complex field in which applied solutions should be flexible and include significant adaptation capabilities. These environments are related to problems where multiple users and devices may interact, and where simple and local solutions could possibly generate good results, but may not be effective with regards to use and interaction. There are many techniques that can be employed to face this kind of problems, from CORBA to multi-agent systems, passing by web-services and SOA, among others. All those methodologies have their advantages and disadvantages that are properly analyzed in this documents, to finally explain the new architecture presented as a solution for distributed environment problems. The new architecture for solving complex solutions in distributed environments presented here is called OBaMADE: Organization Based Multiagent Architecture for Distributed Environments. It is a multiagent architecture based on the organizations of agents paradigm, where the agents in the architecture are structured into organizations to improve their organizational capabilities. The reasoning power of the architecture is based on the Case-Based Reasoning methology, being implemented in a internal organization that uses agents to create services to solve the external request made by the users. The OBaMADE architecture has been successfully applied to two different case studies where its prediction capabilities have been properly checked. Those case studies have showed optimistic results and, being complex systems, have demonstrated the abstraction and generalizations capabilities of the architecture. Nevertheless OBaMADE is intended to be able to solve much other kind of problems in distributed environments scenarios. It should be applied to other varieties of situations and to other knowledge fields to fully develop its potencial.[ES]Los entornos distribuidos representan un campo de conocimiento complejo en el que las soluciones a aplicar deben ser flexibles y deben contar con gran capacidad de adaptaciĂłn. Este tipo de entornos estĂĄ normalmente relacionado con problemas donde varios usuarios y dispositivos entran en juego. Para solucionar dichos problemas, pueden utilizarse sistemas locales que, aunque ofrezcan buenos resultados en tĂ©rminos de calidad de los mismos, no son tan efectivos en cuanto a la interacciĂłn y posibilidades de uso. Existen mĂșltiples tĂ©cnicas que pueden ser empleadas para resolver este tipo de problemas, desde CORBA a sistemas multiagente, pasando por servicios web y SOA, entre otros. Todas estas mitologĂ­as tienen sus ventajas e inconvenientes, que se analizan en este documento, para explicar, finalmente, la nueva arquitectura presentada como una soluciĂłn para los problemas generados en entornos distribuidos. La nueva arquitectura aquĂ­ se llama OBaMADE, que es el acrĂłnimo del inglĂ©s Organization Based Multiagent Architecture for Distributed Environments (Arquitectura Multiagente Basada en Organizaciones para Entornos Distribuidos). Se trata de una arquitectura multiagente basasa en el paradigma de las organizaciones de agente, donde los agentes que forman parte de la arquitectura se estructuran en organizaciones para mejorar sus capacidades organizativas. La capacidad de razonamiento de la arquitectura estĂĄ basada en la metodologĂ­a de razonamiento basado en casos, que se ha implementado en una de las organizaciones internas de la arquitectura por medio de agentes que crean servicios que responden a las solicitudes externas de los usuarios. La arquitectura OBaMADE se ha aplicado de forma exitosa a dos casos de estudio diferentes, en los que se han demostrado sus capacidades predictivas. Aplicando OBaMADE a estos casos de estudio se han obtenido resultados esperanzadores y, al ser sistemas complejos, se han demostrado las capacidades tanto de abstracciĂłn como de generalizaciĂłn de la arquitectura presentada. Sin embargo, esta arquitectura estĂĄ diseñada para poder ser aplicada a mĂĄs tipo de problemas de entornos distribuidos. Debe ser aplicada a mĂĄs variadas situaciones y a otros campos de conocimiento para desarrollar completamente el potencial de esta arquitectura

    Agents and Robots for Reliable Engineered Autonomy

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    This book contains the contributions of the Special Issue entitled "Agents and Robots for Reliable Engineered Autonomy". The Special Issue was based on the successful first edition of the "Workshop on Agents and Robots for reliable Engineered Autonomy" (AREA 2020), co-located with the 24th European Conference on Artificial Intelligence (ECAI 2020). The aim was to bring together researchers from autonomous agents, as well as software engineering and robotics communities, as combining knowledge from these three research areas may lead to innovative approaches that solve complex problems related to the verification and validation of autonomous robotic systems

    On the role of Computational Logic in Data Science: representing, learning, reasoning, and explaining knowledge

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    In this thesis we discuss in what ways computational logic (CL) and data science (DS) can jointly contribute to the management of knowledge within the scope of modern and future artificial intelligence (AI), and how technically-sound software technologies can be realised along the path. An agent-oriented mindset permeates the whole discussion, by stressing pivotal role of autonomous agents in exploiting both means to reach higher degrees of intelligence. Accordingly, the goals of this thesis are manifold. First, we elicit the analogies and differences among CL and DS, hence looking for possible synergies and complementarities along 4 major knowledge-related dimensions, namely representation, acquisition (a.k.a. learning), inference (a.k.a. reasoning), and explanation. In this regard, we propose a conceptual framework through which bridges these disciplines can be described and designed. We then survey the current state of the art of AI technologies, w.r.t. their capability to support bridging CL and DS in practice. After detecting lacks and opportunities, we propose the notion of logic ecosystem as the new conceptual, architectural, and technological solution supporting the incremental integration of symbolic and sub-symbolic AI. Finally, we discuss how our notion of logic ecosys- tem can be reified into actual software technology and extended towards many DS-related directions
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