Abstract
In this paper we present a cognition-enabled control framework for robot control in mixed human-robot teams performing rescue missions after avalanches. We could focus on two key reasoning mechanisms: First, reasoning about the robot capabilities, which allow them to make best use of the hardware and software components they are equipped with. Second, context-directed interpretation of vague commands, which enables the human leader of the rescue team to state tasks naturally. Simulation-based reasoning mechanisms then refine the vague and ambiguous instructions in the given capability context to appropriate task interpretations. We could show that by employing these reasoning mechanisms we can specify generic plans that automatically adapt themselves to the robotic agent executing them.
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Notes
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Web Ontology Language http://www.w3.org/standards/techs/owl#w3c_all.
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at-location and perceive are macros taken from the cram_plan_library which is part of the stack cram_highlevel.
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Acknowledgments
This work is supported in part by the EU FP7 project SHERPA (grant number #600958).
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Yazdani, F., Brieber, B., Beetz, M. (2016). Cognition-Enabled Robot Control for Mixed Human-Robot Rescue Teams. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_98
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