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Hierarchical Hybrid Planning in a Mobile Service Robot

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https://osnadocs.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2017011315255
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dc.creatorStock, Sebastian
dc.creatorMansouri, Masoumeh
dc.creatorPecora, Federico
dc.creatorHertzberg, Joachim
dc.date.accessioned2017-01-13T09:48:17Z
dc.date.available2017-01-13T09:48:17Z
dc.date.issued2017-01-13T09:48:17Z
dc.identifier.citationIn: Proceedings of KI 2015: Advances in Artificial Intelligence, 21.9.-25.9.2015,S. 309-315. Dresden, Springer, 2015. Lecture Notes in Computer Science ; 9324
dc.identifier.urihttps://osnadocs.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2017011315255-
dc.description.abstractPlanning with diverse knowledge, i.e., hybrid planning, is essential for robotic applications. However, powerful heuristics are needed to reason efficiently in the resulting large search spaces. HTN planning provides a means to reduce the search space; furthermore, meta-CSP search has shown promise in hybrid domains, both wrt. search and online plan adaptation. In this paper we combine the two approaches by implementing HTN-style task decomposition as a meta-constraint in a meta-CSP search, resulting in an HTN planner able to handle very rich domain knowledge. The planner produces partial-order plans and if several goal tasks are given, subtasks can be shared, leading to shorter plans. We demonstrate the straightforward integration of different kinds of knowledge for causal, temporal and resource knowledge as well as knowledge provided by an external path planner. The resulting online planner, CHIMP, is integrated in a plan-based robot control system and is demonstrated to physically guide a PR2 roboteng
dc.relationhttp://dx.doi.org/10.1007/978-3-319-24489-1_28
dc.subjectRobot planningeng
dc.subjectHierarchical task networkseng
dc.subjectCognitive roboticseng
dc.subject.ddc000 - Informatik, Wissen, Systeme
dc.titleHierarchical Hybrid Planning in a Mobile Service Roboteng
dc.typeEinzelbeitrag in einer wissenschaftlichen Zeitschrift [article]
dc.identifier.doi10.1007/978-3-319-24489-1_28
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