A Graph Based Hybrid Approach of Offline Pre-planning and Online Re-planning for Efficient Assembly under Realtime Constraints View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2013

AUTHORS

Daniel Ewert , Sebastian Thelen , Ralph Kunze , Marcel Mayer , Daniel Schilberg , Sabina Jeschke

ABSTRACT

Assembly tasks, e.g. the assembly of an automobile headlight, are a big challenge for nowadays planning systems. Depending on the problem domain, a planner has to deal with a huge number of objects which can be combined in several ways. Uncertainty about the outcome of actions and the availability of parts to be assembled even worsens the problem. As a result, classic approaches have shown to be of little use for reactive (online) planning during assembly, due to the huge computational complexity. The approach proposed in this paper bypasses this problem by calculating the complex planning problems, e.g. which parts must be mounted in which sequence, prior to the actual assembly. During assembly the precalculated solutions are then used to provide fast decisions allowing an efficient execution of the assembly. Within this paper this online planning combined with offline planning and the assessment of realtime constraints during assembly could be executed in the future will be described. More... »

PAGES

623-635

References to SciGraph publications

  • 2008. A Model for Adaptively Generating Assembly Instructions Using State-based Graphs in MANUFACTURING SYSTEMS AND TECHNOLOGIES FOR THE NEW FRONTIER
  • 1993-08. Automated generation of assembly sequence based on geometric and functional reasoning in JOURNAL OF INTELLIGENT MANUFACTURING
  • Book

    TITLE

    Automation, Communication and Cybernetics in Science and Engineering 2011/2012

    ISBN

    978-3-642-33388-0
    978-3-642-33389-7

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-33389-7_49

    DOI

    http://dx.doi.org/10.1007/978-3-642-33389-7_49

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1034836333


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