TU Darmstadt / ULB / TUbiblio

Online interaction of a human supervisor with multi-robot task allocation

Kurowski, Karen ; Stryk, Oskar von (2015)
Online interaction of a human supervisor with multi-robot task allocation.
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

In this paper an approach is presented that allows a human supervisor to efficiently interact with task allocation in a multi-robot team (MRTA). The interaction is based on online modification of the setting of the employed MRTA optimization algorithm during its computation. For the example of a computationally expensive mixed-integer linear programming algorithm it is demonstrated how to achieve up to optimal solution quality, while simultaneously reducing the required calculation time compared to a fully autonomous optimization. The supervisor is enabled to rate feasible, intermediate solutions based on objective or subjective quality criteria and personal expertise. In that way, also suboptimal solutions can be chosen to be satisfactory, and the solver can be terminated without the need to wait for the completion of the computation of the optimal solution. An event based communication concept with queries is used as an efficient means of implementation of the interaction. Furthermore, the supervisor can support the MRTA solver in finding good solutions by defining crucial parts of the solution structure. These intuitive commands are internally translated into constraints and are added to the problem as lazy constraints. This combination of human expertise and state-of-the-art optimization algorithms allows to achieve up to potentially optimal task allocation in much shorter time.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2015
Autor(en): Kurowski, Karen ; Stryk, Oskar von
Art des Eintrags: Bibliographie
Titel: Online interaction of a human supervisor with multi-robot task allocation
Sprache: Deutsch
Publikationsjahr: 2015
Verlag: Springer-Verlag
Buchtitel: Intelligent Autonomous Systems 13 - Proc. 13th International Conference on Intelligent Autonomous Systems (IAS-13)
Reihe: Intelligent Systems and Computing
Band einer Reihe: 302
URL / URN: https://link.springer.com/chapter/10.1007%2F978-3-319-08338-...
Zugehörige Links:
Kurzbeschreibung (Abstract):

In this paper an approach is presented that allows a human supervisor to efficiently interact with task allocation in a multi-robot team (MRTA). The interaction is based on online modification of the setting of the employed MRTA optimization algorithm during its computation. For the example of a computationally expensive mixed-integer linear programming algorithm it is demonstrated how to achieve up to optimal solution quality, while simultaneously reducing the required calculation time compared to a fully autonomous optimization. The supervisor is enabled to rate feasible, intermediate solutions based on objective or subjective quality criteria and personal expertise. In that way, also suboptimal solutions can be chosen to be satisfactory, and the solver can be terminated without the need to wait for the completion of the computation of the optimal solution. An event based communication concept with queries is used as an efficient means of implementation of the interaction. Furthermore, the supervisor can support the MRTA solver in finding good solutions by defining crucial parts of the solution structure. These intuitive commands are internally translated into constraints and are added to the problem as lazy constraints. This combination of human expertise and state-of-the-art optimization algorithms allows to achieve up to potentially optimal task allocation in much shorter time.

Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Simulation, Systemoptimierung und Robotik
Hinterlegungsdatum: 20 Jun 2016 23:26
Letzte Änderung: 05 Apr 2019 07:03
PPN:
Zugehörige Links:
Export:
Suche nach Titel in: TUfind oder in Google
Frage zum Eintrag Frage zum Eintrag

Optionen (nur für Redakteure)
Redaktionelle Details anzeigen Redaktionelle Details anzeigen