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Integrated robot task and motion planning in belief space

dc.date.accessioned2012-07-03T18:00:04Z
dc.date.accessioned2018-11-26T22:26:51Z
dc.date.available2012-07-03T18:00:04Z
dc.date.available2018-11-26T22:26:51Z
dc.date.issued2012-07-03
dc.identifier.urihttp://hdl.handle.net/1721.1/71529
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/71529
dc.description.abstractIn this paper, we describe an integrated strategy for planning, perception, state-estimation and action in complex mobile manipulation domains. The strategy is based on planning in the belief space of probability distribution over states. Our planning approach is based on hierarchical goal regression (pre-image back-chaining). We develop a vocabulary of fluents that describe sets of belief states, which are goals and subgoals in the planning process. We show that a relatively small set of symbolic operators lead to task-oriented perception in support of the manipulation goals. An implementation of this method is demonstrated in simulation and on a real PR2 robot, showing robust, flexible solution of mobile manipulation problems with multiple objects and substantial uncertainty.en_US
dc.format.extent80 p.en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs 3.0 Unporteden
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/
dc.titleIntegrated robot task and motion planning in belief spaceen_US


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Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported