Planning efficient and coordinated policies for a team of robots is a computationally demanding problem, especially when the system faces uncertainty in the outcome or duration of actions. In practice, approximation methods are usually employed to plan reasonable team policies in an acceptable time. At the same time, many typical robotic tasks include a repetitive pattern. On the one hand, this multiplies the increased cost of inefficient solutions. But on the other hand, it also provides the potential for improving an initial, inefficient solution over time. In this paper, we consider the case that a single mission specification is given to a multi-robot system, describing repetitive tasks which allow the robots to parallelize work. We propose here a decentralized coordination scheme which enables the robots to decompose the full specification, execute distributed tasks, and improve their strategy over time. ; Part of proceeding: ISBN 978-0-9923747-4-7 QC 20180612


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Improving Multi-Robot Behavior Using Learning-Based Receding Horizon Task Allocation


    Beteiligte:

    Erscheinungsdatum :

    2018-01-01



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    Receding Horizon Task and Motion Planning in Changing Environments

    Castaman N. / Pagello E. / Menegatti E. et al. | BASE | 2021

    Freier Zugriff

    Receding horizon state estimator

    AHN HEEJIN / DANIELSON CLAUS | Europäisches Patentamt | 2021

    Freier Zugriff

    RECEDING HORIZON STATE ESTIMATOR

    AHN HEEJIN / DANIELSON CLAUS | Europäisches Patentamt | 2021

    Freier Zugriff

    Receding Horizon State Estimator

    AHN HEEJIN / DANIELSON CLAUS | Europäisches Patentamt | 2021

    Freier Zugriff

    RECEDING HORIZON STATE ESTIMATOR

    AHN HEEJIN / DANIELSON CLAUS | Europäisches Patentamt | 2022

    Freier Zugriff