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Using Geometric Constraints for Efficiently Combining Task and Motion Planning
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS)
Örebro universitet, Institutionen för naturvetenskap och teknik. INRIA Rhône-Alpes, France. (AASS)
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS)ORCID-id: 0000-0001-8229-1363
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS)
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(engelsk)Inngår i: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176Artikkel i tidsskrift (Fagfellevurdert) Submitted
Abstract [en]

We propose a constraint-based approach to address a class of problems encountered in Combined Task and Motion Planning (CTAMP), which we call geometrically constrained problems. CTAMP is a hybrid planning process in which task planning and geometric reasoning are interleaved. During this process, symbolic action sequences generated by a task planner are geometrically evaluated. This geometric evaluation is a search problem per se, which we refer to asgeometric backtrack search. In geometrically constrained problems, a significant computational effort is spent on geometric backtrack search, which impairs search at the task-level. At the basis of our approach to address this problem, is the introduction of an intermediate layer between task planning and geometric reasoning. A set of constraints is automatically generated from the symbolic action sequences to evaluate, and combined with a set of constraints derived from the kinematic model of the robot. The resulting constraint network is then used to prune the search space during geometric backtrack search. We present experimental evidence that our approach significantly reduces the complexity of geometric backtrack search on various types of problem.

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URN: urn:nbn:se:oru:diva-29161OAI: oai:DiVA.org:oru-29161DiVA, id: diva2:623111
Forskningsfinansiär
EU, FP7, Seventh Framework Programme, 248273Tilgjengelig fra: 2013-05-24 Laget: 2013-05-24 Sist oppdatert: 2018-01-11bibliografisk kontrollert

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Lagriffoul, FabienDimitrov, DimitarSaffiotti, AlessandroBidot, JulienKarlsson, Lars

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