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Online Interaction of a Human Supervisor with Multi-Robot Task Allocation

  • Karen Kurowski
  • Oskar von StrykEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)

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.

Keywords

Planning Horizon Task Allocation Soft Constraint Hard Constraint Query Mode 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research has been supported by the German Research Foundation (DFG) within GRK 1362 “Cooperative, adaptive and responsive monitoring in mixed mode environments”.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Simulation, Systems Optimization and Robotics Group, CS DepartmentTechnische Universität DarmstadtDarmstadtGermany

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